From cc7538eae9e4a392b3d0a1a545f530ca21a60b64 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 11:06:49 +0000 Subject: [PATCH 01/18] Check the simulation tables in the Simulate workflow `simulate.py --check` compares the AMICI simulations at the nominal parameters against `v1/simulations.tsv` and exits with status 1 if they differ (default rtol 1e-3, atol 1e-6, configurable with --rtol/--atol). Rows are matched by observable, conditions and time point (to 6 significant digits), replicates by their order; observable IDs of a flattened problem in the table are mapped back to the original ones. `--output` writes the simulations as PEtab simulation table. The Simulate workflow runs the check for every problem, also on pull requests that touch the problems, and fails on any mismatch, except for the problems in KNOWN_FAILURES, whose tables do not match yet. A known failure that passes fails the job too, so that the list is kept current. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/simulate.yml | 49 ++++++- src/python/simulate.py | 230 ++++++++++++++++++++++++++++++++- 2 files changed, 275 insertions(+), 4 deletions(-) diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index f8346e69..a0e0925e 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -3,6 +3,11 @@ on: push: branches: - master + pull_request: + paths: + - 'problems/**' + - 'src/python/simulate.py' + - '.github/workflows/simulate.yml' workflow_dispatch: merge_group: schedule: @@ -30,12 +35,52 @@ jobs: pip install petab pip install "git+https://github.com/dweindl/amici.git@6a85422ad233fc268fb71272bba39f5b82db387a#egg=amici&subdirectory=python/sdist" - - name: Simulate with nominal parameters + - name: Simulate with nominal parameters and compare to the simulation tables env: AMICI_PARALLEL_COMPILE: "" + # Problems whose simulation tables are known not to match the + # simulations at the nominal parameters. They are simulated and + # reported, but do not fail the job. Remove a problem from this + # list once its simulation table has been fixed. + KNOWN_FAILURES: >- + Blasi_CellSystems2016 + Chen_MSB2009 + Crauste_CellSystems2017 + Fujita_SciSignal2010 + Lang_PLOSComputBiol2024 + Liu_IFACPapersOnLine2025 + Perelson_Science1996 + Raia_CancerResearch2011 + Raimundez_PCB2020 + Sneyd_PNAS2002 + Zhao_QuantBiol2020 + Zheng_PNAS2012 run: | + failed=() + known_failed=() + fixed=() for problem in `ls -1 problems/`; do - python src/python/simulate.py "$problem" -j + known=false + [[ " $KNOWN_FAILURES " == *" $problem "* ]] && known=true + if python src/python/simulate.py "$problem" -j --check; then + $known && fixed+=("$problem") + elif $known; then + known_failed+=("$problem") + else + failed+=("$problem") + fi echo echo done + + { + echo "### Comparison to the simulation tables" + echo + echo "Failed: ${failed[*]:-none}" + echo + echo "Known failures (\`KNOWN_FAILURES\`): ${known_failed[*]:-none}" + echo + echo "Known failures that now pass, to be removed from \`KNOWN_FAILURES\`: ${fixed[*]:-none}" + } | tee -a "$GITHUB_STEP_SUMMARY" + + [ ${#failed[@]} -eq 0 ] && [ ${#fixed[@]} -eq 0 ] diff --git a/src/python/simulate.py b/src/python/simulate.py index e867a201..85b7fab5 100755 --- a/src/python/simulate.py +++ b/src/python/simulate.py @@ -3,23 +3,43 @@ Script to simulate a model using AMICI. Basic usage: simulate.py +Use ``--check`` to compare the simulations against the simulation table of +the problem (``v1/simulations.tsv``); the script then exits with a non-zero +status if they differ. See --help for more options. """ import os +import sys import argparse import benchmark_models_petab import amici.sim.sundials as ass from amici.importers.petab.v1 import ( import_petab_problem, + rdatas_to_simulation_df, simulate_petab, LLH, RDATAS, ) import logging +import numpy as np +import pandas as pd +import petab.v1.C as petab_C from petab.v1.lint import measurement_table_has_timepoint_specific_mappings from petab.v1.core import flatten_timepoint_specific_output_overrides +#: Columns that identify a row of a measurement/simulation table. +KEY_COLUMNS = [ + petab_C.OBSERVABLE_ID, + petab_C.PREEQUILIBRATION_CONDITION_ID, + petab_C.SIMULATION_CONDITION_ID, + petab_C.TIME, +] +#: Significant digits of the time points used to match rows; some +#: simulation tables carry the time points with more digits than the +#: measurement tables. +TIME_SIGNIFICANT_DIGITS = 6 + def parse_cli_args(): """Parse command line arguments.""" @@ -43,6 +63,33 @@ def parse_cli_args(): "If omitted, threading is left unchanged." ), ) + parser.add_argument( + "--check", + action="store_true", + help=( + "Compare the simulations against the simulation table of the " + "problem and exit with status 1 if they differ. Problems " + "without a simulation table are skipped." + ), + ) + parser.add_argument( + "--rtol", + type=float, + default=1e-3, + help="Relative tolerance for --check (default: %(default)s).", + ) + parser.add_argument( + "--atol", + type=float, + default=1e-6, + help="Absolute tolerance for --check (default: %(default)s).", + ) + parser.add_argument( + "--output", + type=str, + default=None, + help="Write the simulations as PEtab simulation table to this file.", + ) args = parser.parse_args() args.num_threads = ( @@ -56,6 +103,146 @@ def parse_cli_args(): return args +def _row_keys(df: pd.DataFrame) -> pd.DataFrame: + """Get the columns that identify the rows of a measurement/simulation + table, normalized for matching rows of different tables. + + Rows with identical keys (e.g. replicates) are told apart by their + order of appearance. + """ + keys = pd.DataFrame(index=df.index) + for column in KEY_COLUMNS: + if column == petab_C.TIME: + keys[column] = [ + f"{float(t):.{TIME_SIGNIFICANT_DIGITS}g}" for t in df[column] + ] + elif column in df: + keys[column] = [ + "" if pd.isna(value) else str(value) for value in df[column] + ] + else: + keys[column] = "" + keys["replicate"] = keys.groupby(KEY_COLUMNS).cumcount() + return keys + + +def unflatten_observable_ids( + simulation_df: pd.DataFrame, observable_ids +) -> pd.DataFrame: + """Replace observable IDs of a flattened problem by the original ones. + + Simulation tables of problems with timepoint-specific overrides may use + the observable IDs of the flattened problem, + ``____``, see + :func:`petab.v1.core.flatten_timepoint_specific_output_overrides`. + """ + observable_ids = sorted(observable_ids, key=len, reverse=True) + + def unflatten(id_): + if id_ in observable_ids: + return id_ + for observable_id in observable_ids: + if id_.startswith(f"{observable_id}__"): + return observable_id + return id_ + + if petab_C.OBSERVABLE_ID not in simulation_df: + return simulation_df + simulation_df = simulation_df.copy() + simulation_df[petab_C.OBSERVABLE_ID] = [ + unflatten(str(id_)) for id_ in simulation_df[petab_C.OBSERVABLE_ID] + ] + return simulation_df + + +def compare_simulations( + simulation_df: pd.DataFrame, + expected_df: pd.DataFrame, + rtol: float, + atol: float, +) -> bool: + """Compare simulations against a reference simulation table. + + Parameters + ---------- + simulation_df: + The simulations. Must have the column ``simulation`` and the columns + of :data:`KEY_COLUMNS`. + expected_df: + The reference simulation table. + rtol, atol: + Relative and absolute tolerance, see :func:`numpy.isclose`. + + Returns + ------- + Whether the simulations agree. + """ + for column in [petab_C.OBSERVABLE_ID, petab_C.SIMULATION_CONDITION_ID]: + if column not in expected_df: + print(f"The simulation table has no column `{column}`.") + return False + + actual = _row_keys(simulation_df) + actual["actual"] = simulation_df[petab_C.SIMULATION].to_numpy(float) + expected = _row_keys(expected_df) + expected["expected"] = pd.to_numeric( + expected_df[petab_C.SIMULATION], errors="coerce" + ).to_numpy(float) + + merged = actual.merge( + expected, + on=KEY_COLUMNS + ["replicate"], + how="outer", + indicator=True, + sort=False, + ) + ok = True + + for side, description in [ + ("left_only", "simulated, but not in the simulation table"), + ("right_only", "in the simulation table, but not simulated"), + ]: + unmatched = merged[merged["_merge"] == side] + if not unmatched.empty: + ok = False + print(f"{len(unmatched)} rows {description}, e.g.:") + print(unmatched[KEY_COLUMNS].head().to_string(index=False)) + print() + + both = merged[merged["_merge"] == "both"] + close = np.isclose( + both["actual"], both["expected"], rtol=rtol, atol=atol, equal_nan=True + ) + if not close.all(): + ok = False + mismatches = both[~close].copy() + mismatches["abs_diff"] = ( + mismatches["actual"] - mismatches["expected"] + ).abs() + mismatches["rel_diff"] = mismatches["abs_diff"] / mismatches[ + "expected" + ].abs().replace(0, np.nan) + print( + f"{len(mismatches)} of {len(both)} simulations differ " + f"(rtol={rtol}, atol={atol}); largest differences:" + ) + print( + mismatches.sort_values("abs_diff", ascending=False)[ + KEY_COLUMNS + ["actual", "expected", "abs_diff", "rel_diff"] + ] + .head(10) + .to_string(index=False) + ) + print() + + if ok: + print( + f"All {len(both)} simulations agree with the simulation table " + f"(rtol={rtol}, atol={atol})." + ) + return ok + + def main(): """Simulate a PEtab problem with AMICI using nominal parameter values.""" args = parse_cli_args() @@ -65,10 +252,17 @@ def main(): print("-" * len(args.problem_id), flush=True) problem = benchmark_models_petab.get_problem(args.problem_id) + expected_df = benchmark_models_petab.get_simulation_df(args.problem_id) + if args.check and expected_df is None: + print("No simulation table, skipping the simulation.") + print() + return - if measurement_table_has_timepoint_specific_mappings( + original_measurement_df = problem.measurement_df.copy() + flattened = measurement_table_has_timepoint_specific_mappings( problem.measurement_df, - ): + ) + if flattened: flatten_timepoint_specific_output_overrides(problem) model = import_petab_problem( @@ -91,6 +285,38 @@ def main(): print("Total log-likelihood:", res[LLH]) print() + if not (args.check or args.output): + return + + # The simulations in the order of the measurement table, with the + # observable IDs of the original (not flattened) problem. + simulation_df = rdatas_to_simulation_df( + res[RDATAS], model, problem.measurement_df + ).loc[original_measurement_df.index] + if flattened: + simulation_df[petab_C.OBSERVABLE_ID] = original_measurement_df[ + petab_C.OBSERVABLE_ID + ] + + if args.output: + simulation_df.to_csv(args.output, sep="\t", index=False) + print(f"Wrote the simulations to {args.output}.") + print() + + if not args.check: + return + + expected_df = unflatten_observable_ids( + expected_df, original_measurement_df[petab_C.OBSERVABLE_ID].unique() + ) + if not compare_simulations( + simulation_df, expected_df, rtol=args.rtol, atol=args.atol + ): + print(f"FAILED: {args.problem_id}") + print() + sys.exit(1) + print() + if __name__ == "__main__": main() From 5b8acf719747cf0583048912f6431fc8cbc70756 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 12:54:15 +0000 Subject: [PATCH 02/18] Check the solver tolerances and store problem-specific settings `simulate.py --check-tolerances` simulates a second time with rtol and atol divided by 100 and fails if the simulations (relative to the largest absolute simulation of the observable) or the log-likelihood change by more than 1e-5, 100 times below the tolerance of the comparison to the simulation tables. Failed simulations are reported instead of raising. The solver settings (rtol, atol, max_steps) are read from `solver_settings.yaml`: the AMICI defaults, and stricter settings for the problems that do not pass the check with them (Borghans, Crauste, Isensee, Lang, Raimundez, Zheng); Chen gets a larger atol, with which the check at stricter tolerances does not fail. With these settings, the simulations of Crauste, Lang and Raimundez agree with their simulation tables; they are removed from KNOWN_FAILURES. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/simulate.yml | 15 +- src/python/simulate.py | 285 +++++++++++++++++++++++++++----- src/python/solver_settings.yaml | 48 ++++++ 3 files changed, 298 insertions(+), 50 deletions(-) create mode 100644 src/python/solver_settings.yaml diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index a0e0925e..a0dcd5fc 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -35,23 +35,21 @@ jobs: pip install petab pip install "git+https://github.com/dweindl/amici.git@6a85422ad233fc268fb71272bba39f5b82db387a#egg=amici&subdirectory=python/sdist" - - name: Simulate with nominal parameters and compare to the simulation tables + - name: Simulate, check the solver tolerances and compare to the simulation tables env: AMICI_PARALLEL_COMPILE: "" # Problems whose simulation tables are known not to match the - # simulations at the nominal parameters. They are simulated and - # reported, but do not fail the job. Remove a problem from this - # list once its simulation table has been fixed. + # simulations at the nominal parameters (with tolerances that pass + # the tolerance check). They are simulated and reported, but do not + # fail the job. Remove a problem from this list once its simulation + # table has been fixed. KNOWN_FAILURES: >- Blasi_CellSystems2016 Chen_MSB2009 - Crauste_CellSystems2017 Fujita_SciSignal2010 - Lang_PLOSComputBiol2024 Liu_IFACPapersOnLine2025 Perelson_Science1996 Raia_CancerResearch2011 - Raimundez_PCB2020 Sneyd_PNAS2002 Zhao_QuantBiol2020 Zheng_PNAS2012 @@ -62,7 +60,8 @@ jobs: for problem in `ls -1 problems/`; do known=false [[ " $KNOWN_FAILURES " == *" $problem "* ]] && known=true - if python src/python/simulate.py "$problem" -j --check; then + if python src/python/simulate.py "$problem" -j --check \ + --check-tolerances; then $known && fixed+=("$problem") elif $known; then known_failed+=("$problem") diff --git a/src/python/simulate.py b/src/python/simulate.py index 85b7fab5..1d9a438a 100755 --- a/src/python/simulate.py +++ b/src/python/simulate.py @@ -22,7 +22,10 @@ RDATAS, ) import logging +from pathlib import Path + import numpy as np +import yaml import pandas as pd import petab.v1.C as petab_C from petab.v1.lint import measurement_table_has_timepoint_specific_mappings @@ -39,6 +42,14 @@ #: simulation tables carry the time points with more digits than the #: measurement tables. TIME_SIGNIFICANT_DIGITS = 6 +#: Default and problem-specific solver settings. +SOLVER_SETTINGS_FILE = Path(__file__).with_name("solver_settings.yaml") +#: For ``--check-tolerances``: the factor by which the tolerances are made +#: stricter, and the largest acceptable relative change of the simulations +#: and of the log-likelihood; 100 times below the default relative tolerance +#: of the comparison to the simulation tables (``--rtol``). +TOLERANCE_CHECK_FACTOR = 100 +TOLERANCE_CHECK_RTOL = 1e-5 def parse_cli_args(): @@ -72,17 +83,32 @@ def parse_cli_args(): "without a simulation table are skipped." ), ) + parser.add_argument( + "--check-tolerances", + action="store_true", + help=( + "Check that the solver tolerances are strict enough: simulate " + "again with stricter tolerances and exit with status 1 if the " + "simulations or the log-likelihood change substantially." + ), + ) parser.add_argument( "--rtol", type=float, default=1e-3, - help="Relative tolerance for --check (default: %(default)s).", + help=( + "Relative tolerance of the comparison for --check " + "(default: %(default)s)." + ), ) parser.add_argument( "--atol", type=float, default=1e-6, - help="Absolute tolerance for --check (default: %(default)s).", + help=( + "Absolute tolerance of the comparison for --check " + "(default: %(default)s)." + ), ) parser.add_argument( "--output", @@ -243,6 +269,179 @@ def compare_simulations( return ok +def get_solver_settings(problem_id: str) -> dict: + """Get the solver settings for a problem. + + The defaults of :data:`SOLVER_SETTINGS_FILE`, updated by the settings + for the problem, if any. + """ + with open(SOLVER_SETTINGS_FILE) as f: + settings = yaml.safe_load(f) + return { + **settings["default"], + **(settings.get("problems") or {}).get(problem_id, {}), + } + + +def simulate( + problem, + model, + measurement_df: pd.DataFrame, + settings: dict, + num_threads: int = 1, + verbose: bool = True, +): + """Simulate a PEtab problem at the nominal parameters. + + Parameters + ---------- + problem: + The PEtab problem the AMICI model was imported from, possibly + flattened. + model: + The AMICI model. + measurement_df: + The measurement table of the original problem (before flattening). + settings: + The solver settings, see :data:`SOLVER_SETTINGS_FILE`. + num_threads: + Number of threads to simulate the conditions in parallel. + verbose: + Whether to print the log-likelihood of every condition. + + Returns + ------- + The result of :func:`simulate_petab` and the simulations as PEtab + simulation table, in the order and with the observable IDs of + ``measurement_df``, or ``None`` if a simulation failed. + """ + solver = model.create_solver() + solver.set_return_data_reporting_mode( + ass.RDataReporting.observables_likelihood + ) + solver.set_relative_tolerance(settings["rtol"]) + solver.set_absolute_tolerance(settings["atol"]) + solver.set_max_steps(int(settings["max_steps"])) + res = simulate_petab( + problem, model, solver=solver, num_threads=num_threads + ) + + if verbose: + for rdata in res[RDATAS]: + print( + f"{rdata.id}: llh = {rdata.llh}, chi2 = {rdata.chi2}, " + f"status = {rdata.status}" + ) + print() + + failed = [rdata for rdata in res[RDATAS] if rdata.status != 0] + if failed: + print( + f"The simulation of {len(failed)} conditions failed, e.g. " + f"{failed[0].id} with status {failed[0].status}." + ) + return res, None + + # The simulations in the order of the measurement table, with the + # observable IDs of the original (not flattened) problem. + simulation_df = rdatas_to_simulation_df( + res[RDATAS], model, problem.measurement_df + ).loc[measurement_df.index] + simulation_df[petab_C.OBSERVABLE_ID] = measurement_df[ + petab_C.OBSERVABLE_ID + ] + return res, simulation_df + + +def check_tolerances( + problem, + model, + measurement_df: pd.DataFrame, + settings: dict, + simulation_df: pd.DataFrame, + llh: float, + num_threads: int = 1, +) -> bool: + """Check that the solver tolerances are strict enough. + + Simulates again with the tolerances divided by + :data:`TOLERANCE_CHECK_FACTOR` and checks that neither the simulations + nor the log-likelihood change by more than :data:`TOLERANCE_CHECK_RTOL` + (relative to the largest absolute simulation of the observable, for the + simulations). + + Returns + ------- + Whether the tolerances are strict enough. + """ + stricter = { + **settings, + "rtol": settings["rtol"] / TOLERANCE_CHECK_FACTOR, + "atol": settings["atol"] / TOLERANCE_CHECK_FACTOR, + } + res_strict, simulation_df_strict = simulate( + problem, + model, + measurement_df, + stricter, + num_threads=num_threads, + verbose=False, + ) + if simulation_df_strict is None: + print( + f"The simulation with stricter tolerances " + f"(rtol={stricter['rtol']:g}, atol={stricter['atol']:g}) failed. " + f"Increase max_steps for the problem in " + f"{SOLVER_SETTINGS_FILE.name}." + ) + print() + return False + llh_strict = res_strict[LLH] + + simulation = simulation_df[petab_C.SIMULATION].to_numpy(float) + simulation_strict = simulation_df_strict[petab_C.SIMULATION].to_numpy( + float + ) + # scale of each observable, to judge the simulations close to zero + scale = ( + pd.Series(np.abs(simulation_strict)) + .groupby(simulation_df[petab_C.OBSERVABLE_ID].to_numpy()) + .transform("max") + .to_numpy() + ) + error = np.abs(simulation - simulation_strict) / np.where( + scale > 0, scale, 1.0 + ) + error[np.isnan(simulation) & np.isnan(simulation_strict)] = 0.0 + error[np.isnan(simulation) != np.isnan(simulation_strict)] = np.inf + max_error = np.max(error, initial=0.0) + llh_error = abs(llh - llh_strict) / max(abs(llh_strict), 1.0) + if np.isnan(llh) and np.isnan(llh_strict): + llh_error = 0.0 + elif np.isnan(llh) or np.isnan(llh_strict): + llh_error = np.inf + + print( + f"Tolerance check: rtol={settings['rtol']:g}, " + f"atol={settings['atol']:g} vs. rtol={stricter['rtol']:g}, " + f"atol={stricter['atol']:g}: " + f"largest change of the simulations {max_error:.2e} (relative to " + f"the observable), of the log-likelihood {llh_error:.2e} " + f"({llh} vs. {llh_strict})." + ) + ok = ( + max_error <= TOLERANCE_CHECK_RTOL and llh_error <= TOLERANCE_CHECK_RTOL + ) + if not ok: + print( + f"The tolerances are not strict enough: the changes exceed " + f"{TOLERANCE_CHECK_RTOL:g}. Set stricter tolerances for the " + f"problem in {SOLVER_SETTINGS_FILE.name}." + ) + print() + return ok + + def main(): """Simulate a PEtab problem with AMICI using nominal parameter values.""" args = parse_cli_args() @@ -253,69 +452,71 @@ def main(): problem = benchmark_models_petab.get_problem(args.problem_id) expected_df = benchmark_models_petab.get_simulation_df(args.problem_id) - if args.check and expected_df is None: + if args.check and expected_df is None and not args.check_tolerances: print("No simulation table, skipping the simulation.") print() return - original_measurement_df = problem.measurement_df.copy() - flattened = measurement_table_has_timepoint_specific_mappings( + settings = get_solver_settings(args.problem_id) + print(f"Solver settings: {settings}") + print() + + measurement_df = problem.measurement_df.copy() + if measurement_table_has_timepoint_specific_mappings( problem.measurement_df, - ) - if flattened: + ): flatten_timepoint_specific_output_overrides(problem) model = import_petab_problem( problem, generate_sensitivity_code=False, verbose=logging.INFO ) - solver = model.create_solver() - solver.set_return_data_reporting_mode( - ass.RDataReporting.observables_likelihood - ) - res = simulate_petab( - problem, model, solver=solver, num_threads=args.num_threads + res, simulation_df = simulate( + problem, + model, + measurement_df, + settings, + num_threads=args.num_threads, ) - - for rdata in res[RDATAS]: - print( - f"{rdata.id}: llh = {rdata.llh}, chi2 = {rdata.chi2}, status = {rdata.status}" - ) - - print() print("Total log-likelihood:", res[LLH]) print() - - if not (args.check or args.output): - return - - # The simulations in the order of the measurement table, with the - # observable IDs of the original (not flattened) problem. - simulation_df = rdatas_to_simulation_df( - res[RDATAS], model, problem.measurement_df - ).loc[original_measurement_df.index] - if flattened: - simulation_df[petab_C.OBSERVABLE_ID] = original_measurement_df[ - petab_C.OBSERVABLE_ID - ] + if simulation_df is None: + print(f"FAILED: {args.problem_id}") + print() + sys.exit(1) if args.output: simulation_df.to_csv(args.output, sep="\t", index=False) print(f"Wrote the simulations to {args.output}.") print() - if not args.check: - return + ok = True + if args.check_tolerances: + ok &= check_tolerances( + problem, + model, + measurement_df, + settings, + simulation_df, + res[LLH], + num_threads=args.num_threads, + ) - expected_df = unflatten_observable_ids( - expected_df, original_measurement_df[petab_C.OBSERVABLE_ID].unique() - ) - if not compare_simulations( - simulation_df, expected_df, rtol=args.rtol, atol=args.atol - ): + if args.check and expected_df is None: + print("No simulation table, skipping the comparison.") + print() + elif args.check: + expected_df = unflatten_observable_ids( + expected_df, measurement_df[petab_C.OBSERVABLE_ID].unique() + ) + ok &= compare_simulations( + simulation_df, expected_df, rtol=args.rtol, atol=args.atol + ) + print() + + if not ok: print(f"FAILED: {args.problem_id}") print() sys.exit(1) - print() if __name__ == "__main__": diff --git a/src/python/solver_settings.yaml b/src/python/solver_settings.yaml new file mode 100644 index 00000000..a16327c8 --- /dev/null +++ b/src/python/solver_settings.yaml @@ -0,0 +1,48 @@ +# Solver settings for `simulate.py`. +# +# `default` applies to every problem; `problems` overrides it for single +# problems. A problem needs an override if its simulations or its +# log-likelihood change substantially with stricter tolerances, see +# `simulate.py --check-tolerances`, which simulates again with rtol and atol +# divided by 100 and requires a relative change below 1e-5. +# +# rtol, atol: relative and absolute integration tolerances (the steady-state +# tolerances follow them, see AMICI's `steady_state_tolerance_factor`) +# max_steps: maximum number of integration steps per simulation +default: + rtol: 1.0e-8 + atol: 1.0e-16 + max_steps: 10000 + +problems: + Borghans_BiophysChem1997: + # default: change 1.1e-5 + rtol: 1.0e-10 + atol: 1.0e-18 + # for the check at rtol 1e-12 + max_steps: 100000 + Chen_MSB2009: + # the default rtol is accurate enough (change 4e-8 at rtol 1e-10, with + # atol 1e-16 and more steps), but with atol 1e-18 the integration fails + atol: 1.0e-12 + Crauste_CellSystems2017: + # change 6e-3 at the default rtol, 2e-4 at rtol 1e-10 + rtol: 1.0e-12 + atol: 1.0e-20 + Isensee_JCB2018: + # default: change 1.9e-5 + rtol: 1.0e-10 + atol: 1.0e-18 + Lang_PLOSComputBiol2024: + # the default number of steps is too few (status -99, simulations NaN) + rtol: 1.0e-10 + atol: 1.0e-18 + max_steps: 100000 + Raimundez_PCB2020: + # change 2.3e-3 at the default rtol, 2.3e-5 at rtol 1e-10 + rtol: 1.0e-12 + atol: 1.0e-20 + Zheng_PNAS2012: + # default: change 1.7e-5 + rtol: 1.0e-10 + atol: 1.0e-18 From ec17205495fdfd277fcd8cfe43818bf29346ff29 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 13:56:43 +0000 Subject: [PATCH 03/18] Simulate a subset of Froehlich in CI, no tolerance check in CI CI only simulates the problems with the solver settings of `solver_settings.yaml` and compares the simulations to the simulation tables; `--check-tolerances` remains a local tool to choose the settings. Froehlich_CellSystems2018 needs rtol 1e-10 (change 2.5e-4 at the default, 2.5e-6 at rtol 1e-10). Simulating all of its 9169 conditions at that tolerance takes about 20 minutes on 4 cores, so a new optional setting, `conditions`, restricts the simulation to evenly spaced conditions (1000 for Froehlich, about 2 minutes) and the comparison to their rows of the simulation table. `--all-conditions` simulates all. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/simulate.yml | 14 +++--- src/python/simulate.py | 75 +++++++++++++++++++++++++++++++++ src/python/solver_settings.yaml | 10 +++++ 3 files changed, 92 insertions(+), 7 deletions(-) diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index a0dcd5fc..b10707d2 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -35,14 +35,15 @@ jobs: pip install petab pip install "git+https://github.com/dweindl/amici.git@6a85422ad233fc268fb71272bba39f5b82db387a#egg=amici&subdirectory=python/sdist" - - name: Simulate, check the solver tolerances and compare to the simulation tables + - name: Simulate and compare to the simulation tables env: AMICI_PARALLEL_COMPILE: "" + # The problems are simulated with the solver settings of + # src/python/solver_settings.yaml. # Problems whose simulation tables are known not to match the - # simulations at the nominal parameters (with tolerances that pass - # the tolerance check). They are simulated and reported, but do not - # fail the job. Remove a problem from this list once its simulation - # table has been fixed. + # simulations at the nominal parameters. They are simulated and + # reported, but do not fail the job. Remove a problem from this list + # once its simulation table has been fixed. KNOWN_FAILURES: >- Blasi_CellSystems2016 Chen_MSB2009 @@ -60,8 +61,7 @@ jobs: for problem in `ls -1 problems/`; do known=false [[ " $KNOWN_FAILURES " == *" $problem "* ]] && known=true - if python src/python/simulate.py "$problem" -j --check \ - --check-tolerances; then + if python src/python/simulate.py "$problem" -j --check; then $known && fixed+=("$problem") elif $known; then known_failed+=("$problem") diff --git a/src/python/simulate.py b/src/python/simulate.py index 1d9a438a..faa39ad0 100755 --- a/src/python/simulate.py +++ b/src/python/simulate.py @@ -9,6 +9,7 @@ See --help for more options. """ +import copy import os import sys import argparse @@ -27,6 +28,7 @@ import numpy as np import yaml import pandas as pd +import petab.v1 as petab import petab.v1.C as petab_C from petab.v1.lint import measurement_table_has_timepoint_specific_mappings from petab.v1.core import flatten_timepoint_specific_output_overrides @@ -92,6 +94,15 @@ def parse_cli_args(): "simulations or the log-likelihood change substantially." ), ) + parser.add_argument( + "--all-conditions", + action="store_true", + help=( + "Simulate all conditions, also for problems with `conditions` " + "in the solver settings, for which by default only a subset of " + "the conditions is simulated." + ), + ) parser.add_argument( "--rtol", type=float, @@ -269,6 +280,47 @@ def compare_simulations( return ok +def select_conditions(problem, measurement_df: pd.DataFrame, n: int): + """Restrict a problem to ``n`` of its simulation conditions. + + The conditions are evenly spaced in the order of + :func:`petab.v1.get_simulation_conditions`. + + Parameters + ---------- + problem: + The PEtab problem the AMICI model was imported from, possibly + flattened. + measurement_df: + The measurement table of the original problem (before flattening). + n: + The number of conditions. + + Returns + ------- + A copy of ``problem`` and ``measurement_df`` with only the measurements + of the selected conditions, and the selected conditions. + """ + conditions = petab.get_simulation_conditions(problem.measurement_df) + if n >= len(conditions): + return problem, measurement_df, conditions + selected = conditions.iloc[ + np.unique(np.linspace(0, len(conditions) - 1, n).round().astype(int)) + ] + columns = list(selected.columns) + in_selected = ( + problem.measurement_df[columns] + .merge(selected, how="left", indicator=True)["_merge"] + .eq("both") + .to_numpy() + ) + index = problem.measurement_df.index[in_selected] + + problem = copy.copy(problem) + problem.measurement_df = problem.measurement_df.loc[index] + return problem, measurement_df.loc[index], selected + + def get_solver_settings(problem_id: str) -> dict: """Get the solver settings for a problem. @@ -470,6 +522,29 @@ def main(): model = import_petab_problem( problem, generate_sensitivity_code=False, verbose=logging.INFO ) + + n_conditions = settings.pop("conditions", None) + if n_conditions is not None and not args.all_conditions: + problem, measurement_df, conditions = select_conditions( + problem, measurement_df, int(n_conditions) + ) + print( + f"Simulating {len(conditions)} of the conditions " + f"(`conditions` in {SOLVER_SETTINGS_FILE.name}; use " + "--all-conditions to simulate all)." + ) + print() + if expected_df is not None: + # compare only the rows of the selected conditions + columns = list(conditions.columns) + keys = _row_keys(measurement_df)[columns].drop_duplicates() + expected_df = expected_df.loc[ + _row_keys(expected_df)[columns] + .merge(keys, how="left", indicator=True)["_merge"] + .eq("both") + .to_numpy() + ] + res, simulation_df = simulate( problem, model, diff --git a/src/python/solver_settings.yaml b/src/python/solver_settings.yaml index a16327c8..b7ce5a92 100644 --- a/src/python/solver_settings.yaml +++ b/src/python/solver_settings.yaml @@ -9,6 +9,10 @@ # rtol, atol: relative and absolute integration tolerances (the steady-state # tolerances follow them, see AMICI's `steady_state_tolerance_factor`) # max_steps: maximum number of integration steps per simulation +# conditions: (optional) simulate only this many simulation conditions, +# evenly spaced, and compare only their rows of the simulation table; for +# problems too expensive to simulate completely in CI +# (`simulate.py --all-conditions` simulates all) default: rtol: 1.0e-8 atol: 1.0e-16 @@ -29,6 +33,12 @@ problems: # change 6e-3 at the default rtol, 2e-4 at rtol 1e-10 rtol: 1.0e-12 atol: 1.0e-20 + Froehlich_CellSystems2018: + # default: change 2.5e-4 + rtol: 1.0e-10 + atol: 1.0e-18 + # all 9169 conditions take about 20 minutes on 4 cores, 1000 about 2 + conditions: 1000 Isensee_JCB2018: # default: change 1.9e-5 rtol: 1.0e-10 From 135e459737d5f3542f421929ff5e332ec1329a64 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 14:22:21 +0000 Subject: [PATCH 04/18] Hardcode the tolerances of the comparison to the simulation tables COMPARISON_RTOL (1e-3) and COMPARISON_ATOL (1e-6) replace the --rtol and --atol options of simulate.py. Co-Authored-By: Claude Opus 5.5 --- src/python/simulate.py | 34 +++++++++------------------------- 1 file changed, 9 insertions(+), 25 deletions(-) diff --git a/src/python/simulate.py b/src/python/simulate.py index faa39ad0..353c1ef1 100755 --- a/src/python/simulate.py +++ b/src/python/simulate.py @@ -44,12 +44,16 @@ #: simulation tables carry the time points with more digits than the #: measurement tables. TIME_SIGNIFICANT_DIGITS = 6 +#: Relative and absolute tolerance of the comparison to the simulation +#: tables (``--check``), see :func:`numpy.isclose`. Looser than the solver +#: tolerances, as the tables were made with different tools and settings. +COMPARISON_RTOL = 1e-3 +COMPARISON_ATOL = 1e-6 #: Default and problem-specific solver settings. SOLVER_SETTINGS_FILE = Path(__file__).with_name("solver_settings.yaml") #: For ``--check-tolerances``: the factor by which the tolerances are made #: stricter, and the largest acceptable relative change of the simulations -#: and of the log-likelihood; 100 times below the default relative tolerance -#: of the comparison to the simulation tables (``--rtol``). +#: and of the log-likelihood; 100 times below :data:`COMPARISON_RTOL`. TOLERANCE_CHECK_FACTOR = 100 TOLERANCE_CHECK_RTOL = 1e-5 @@ -103,24 +107,6 @@ def parse_cli_args(): "the conditions is simulated." ), ) - parser.add_argument( - "--rtol", - type=float, - default=1e-3, - help=( - "Relative tolerance of the comparison for --check " - "(default: %(default)s)." - ), - ) - parser.add_argument( - "--atol", - type=float, - default=1e-6, - help=( - "Absolute tolerance of the comparison for --check " - "(default: %(default)s)." - ), - ) parser.add_argument( "--output", type=str, @@ -195,8 +181,8 @@ def unflatten(id_): def compare_simulations( simulation_df: pd.DataFrame, expected_df: pd.DataFrame, - rtol: float, - atol: float, + rtol: float = COMPARISON_RTOL, + atol: float = COMPARISON_ATOL, ) -> bool: """Compare simulations against a reference simulation table. @@ -583,9 +569,7 @@ def main(): expected_df = unflatten_observable_ids( expected_df, measurement_df[petab_C.OBSERVABLE_ID].unique() ) - ok &= compare_simulations( - simulation_df, expected_df, rtol=args.rtol, atol=args.atol - ) + ok &= compare_simulations(simulation_df, expected_df) print() if not ok: From c12d0579eedaabf3909eed25c6c6f6fd9a948698 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 14:28:02 +0000 Subject: [PATCH 05/18] Explain the purpose of the tolerance check Co-Authored-By: Claude Opus 5.5 --- src/python/simulate.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/python/simulate.py b/src/python/simulate.py index 353c1ef1..37e52406 100755 --- a/src/python/simulate.py +++ b/src/python/simulate.py @@ -51,9 +51,11 @@ COMPARISON_ATOL = 1e-6 #: Default and problem-specific solver settings. SOLVER_SETTINGS_FILE = Path(__file__).with_name("solver_settings.yaml") -#: For ``--check-tolerances``: the factor by which the tolerances are made -#: stricter, and the largest acceptable relative change of the simulations -#: and of the log-likelihood; 100 times below :data:`COMPARISON_RTOL`. +#: For ``--check-tolerances``, which checks that the solver tolerances used +#: to reproduce the simulations are tight enough to ensure sufficient +#: accuracy: the factor by which the tolerances are made stricter, and the +#: largest acceptable relative change of the simulations and of the +#: log-likelihood; 100 times below :data:`COMPARISON_RTOL`. TOLERANCE_CHECK_FACTOR = 100 TOLERANCE_CHECK_RTOL = 1e-5 From e80fee84f13bfd83daff6de291c0a5829c44a891 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 14:29:29 +0000 Subject: [PATCH 06/18] Rename --check and --check-tolerances to --check-simulations and --check-simulation-tolerances. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/simulate.yml | 3 ++- src/python/simulate.py | 37 +++++++++++++++++++-------------- src/python/solver_settings.yaml | 4 ++-- 3 files changed, 25 insertions(+), 19 deletions(-) diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index b10707d2..a57a0dbe 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -61,7 +61,8 @@ jobs: for problem in `ls -1 problems/`; do known=false [[ " $KNOWN_FAILURES " == *" $problem "* ]] && known=true - if python src/python/simulate.py "$problem" -j --check; then + if python src/python/simulate.py "$problem" -j \ + --check-simulations; then $known && fixed+=("$problem") elif $known; then known_failed+=("$problem") diff --git a/src/python/simulate.py b/src/python/simulate.py index 37e52406..5162056b 100755 --- a/src/python/simulate.py +++ b/src/python/simulate.py @@ -3,9 +3,9 @@ Script to simulate a model using AMICI. Basic usage: simulate.py -Use ``--check`` to compare the simulations against the simulation table of -the problem (``v1/simulations.tsv``); the script then exits with a non-zero -status if they differ. +Use ``--check-simulations`` to compare the simulations against the +simulation table of the problem (``v1/simulations.tsv``); the script then +exits with a non-zero status if they differ. See --help for more options. """ @@ -45,17 +45,18 @@ #: measurement tables. TIME_SIGNIFICANT_DIGITS = 6 #: Relative and absolute tolerance of the comparison to the simulation -#: tables (``--check``), see :func:`numpy.isclose`. Looser than the solver -#: tolerances, as the tables were made with different tools and settings. +#: tables (``--check-simulations``), see :func:`numpy.isclose`. Looser than +#: the solver tolerances, as the tables were made with different tools and +#: settings. COMPARISON_RTOL = 1e-3 COMPARISON_ATOL = 1e-6 #: Default and problem-specific solver settings. SOLVER_SETTINGS_FILE = Path(__file__).with_name("solver_settings.yaml") -#: For ``--check-tolerances``, which checks that the solver tolerances used -#: to reproduce the simulations are tight enough to ensure sufficient -#: accuracy: the factor by which the tolerances are made stricter, and the -#: largest acceptable relative change of the simulations and of the -#: log-likelihood; 100 times below :data:`COMPARISON_RTOL`. +#: For ``--check-simulation-tolerances``, which checks that the solver +#: tolerances used to reproduce the simulations are tight enough to ensure +#: sufficient accuracy: the factor by which the tolerances are made +#: stricter, and the largest acceptable relative change of the simulations +#: and of the log-likelihood; 100 times below :data:`COMPARISON_RTOL`. TOLERANCE_CHECK_FACTOR = 100 TOLERANCE_CHECK_RTOL = 1e-5 @@ -83,7 +84,7 @@ def parse_cli_args(): ), ) parser.add_argument( - "--check", + "--check-simulations", action="store_true", help=( "Compare the simulations against the simulation table of the " @@ -92,7 +93,7 @@ def parse_cli_args(): ), ) parser.add_argument( - "--check-tolerances", + "--check-simulation-tolerances", action="store_true", help=( "Check that the solver tolerances are strict enough: simulate " @@ -492,7 +493,11 @@ def main(): problem = benchmark_models_petab.get_problem(args.problem_id) expected_df = benchmark_models_petab.get_simulation_df(args.problem_id) - if args.check and expected_df is None and not args.check_tolerances: + if ( + args.check_simulations + and expected_df is None + and not args.check_simulation_tolerances + ): print("No simulation table, skipping the simulation.") print() return @@ -553,7 +558,7 @@ def main(): print() ok = True - if args.check_tolerances: + if args.check_simulation_tolerances: ok &= check_tolerances( problem, model, @@ -564,10 +569,10 @@ def main(): num_threads=args.num_threads, ) - if args.check and expected_df is None: + if args.check_simulations and expected_df is None: print("No simulation table, skipping the comparison.") print() - elif args.check: + elif args.check_simulations: expected_df = unflatten_observable_ids( expected_df, measurement_df[petab_C.OBSERVABLE_ID].unique() ) diff --git a/src/python/solver_settings.yaml b/src/python/solver_settings.yaml index b7ce5a92..d62b4b55 100644 --- a/src/python/solver_settings.yaml +++ b/src/python/solver_settings.yaml @@ -3,8 +3,8 @@ # `default` applies to every problem; `problems` overrides it for single # problems. A problem needs an override if its simulations or its # log-likelihood change substantially with stricter tolerances, see -# `simulate.py --check-tolerances`, which simulates again with rtol and atol -# divided by 100 and requires a relative change below 1e-5. +# `simulate.py --check-simulation-tolerances`, which simulates again with +# rtol and atol divided by 100 and requires a relative change below 1e-5. # # rtol, atol: relative and absolute integration tolerances (the steady-state # tolerances follow them, see AMICI's `steady_state_tolerance_factor`) From d6e21d0d522d278538bb90b8550a8e6c1894cc8a Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 15:02:50 +0000 Subject: [PATCH 07/18] Remove rows without measurements from the Blasi and Zhao simulation tables The simulation tables had rows for data points that are not in the measurement tables; the rows that correspond to measurements agree with the simulations: - Blasi_CellSystems2016: 6 surplus steady-state replicates each of observable_k5k12, observable_k8k12 and observable_k8k16, and 18 rows of observable_k5k16, which has no measurements (36 rows). - Zhao_QuantBiol2020: time points of observable_confirmed_infected after the measured time points of each condition (87 rows). Both are removed from KNOWN_FAILURES. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/simulate.yml | 2 - .../Blasi_CellSystems2016/v1/simulations.tsv | 36 -------- .../Zhao_QuantBiol2020/v1/simulations.tsv | 87 ------------------- 3 files changed, 125 deletions(-) diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index a57a0dbe..3a463550 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -45,14 +45,12 @@ jobs: # reported, but do not fail the job. Remove a problem from this list # once its simulation table has been fixed. KNOWN_FAILURES: >- - Blasi_CellSystems2016 Chen_MSB2009 Fujita_SciSignal2010 Liu_IFACPapersOnLine2025 Perelson_Science1996 Raia_CancerResearch2011 Sneyd_PNAS2002 - Zhao_QuantBiol2020 Zheng_PNAS2012 run: | failed=() diff --git a/problems/Blasi_CellSystems2016/v1/simulations.tsv b/problems/Blasi_CellSystems2016/v1/simulations.tsv index 8fd4f783..b6071e69 100644 --- a/problems/Blasi_CellSystems2016/v1/simulations.tsv +++ b/problems/Blasi_CellSystems2016/v1/simulations.tsv @@ -119,33 +119,6 @@ observable_k5k12 control 0.047962399802456 inf sigma observable_k5k12 control 0.047962399802456 inf sigma observable_k5k12 control 0.047962399802456 inf sigma observable_k5k12 control 0.047962399802456 inf sigma -observable_k5k12 control 0.047962399802456 inf sigma -observable_k5k12 control 0.047962399802456 inf sigma -observable_k5k12 control 0.047962399802456 inf sigma -observable_k5k12 control 0.047962399802456 inf sigma -observable_k5k12 control 0.047962399802456 inf sigma -observable_k5k12 control 0.047962399802456 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k5k16 control 0.003900604877395 inf sigma -observable_k8k12 control 0.007670374014446 inf sigma -observable_k8k12 control 0.007670374014446 inf sigma -observable_k8k12 control 0.007670374014446 inf sigma observable_k8k12 control 0.007670374014446 inf sigma observable_k8k12 control 0.007670374014446 inf sigma observable_k8k12 control 0.007670374014446 inf sigma @@ -158,15 +131,6 @@ observable_k8k12 control 0.007670374014446 inf sigma observable_k8k12 control 0.007670374014446 inf sigma observable_k8k12 control 0.007670374014446 inf sigma observable_k8k12 control 0.007670374014446 inf sigma -observable_k8k12 control 0.007670374014446 inf sigma -observable_k8k12 control 0.007670374014446 inf sigma -observable_k8k12 control 0.007670374014446 inf sigma -observable_k8k16 control 0.004314605919164 inf sigma -observable_k8k16 control 0.004314605919164 inf sigma -observable_k8k16 control 0.004314605919164 inf sigma -observable_k8k16 control 0.004314605919164 inf sigma -observable_k8k16 control 0.004314605919164 inf sigma -observable_k8k16 control 0.004314605919164 inf sigma observable_k8k16 control 0.004314605919164 inf sigma observable_k8k16 control 0.004314605919164 inf sigma observable_k8k16 control 0.004314605919164 inf sigma diff --git a/problems/Zhao_QuantBiol2020/v1/simulations.tsv b/problems/Zhao_QuantBiol2020/v1/simulations.tsv index f92843ca..6c2cee46 100644 --- a/problems/Zhao_QuantBiol2020/v1/simulations.tsv +++ b/problems/Zhao_QuantBiol2020/v1/simulations.tsv @@ -9,25 +9,6 @@ observable_confirmed_infected model1_China_Stage_I 6751.446607931774 36 sd_con observable_confirmed_infected model1_China_Stage_I 7530.370199284482 37 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I observable_confirmed_infected model1_China_Stage_I 8393.869639818662 38 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I observable_confirmed_infected model1_China_Stage_I 9351.054020134636 39 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 10412.017063902333 40 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 11587.943145064462 41 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 12891.225172772729 42 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 14335.594720520885 43 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 15936.266717096447 44 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 17710.09916143024 45 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 19675.77070432931 46 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 21853.97666262693 47 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 24267.646952721356 48 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 26942.186635497943 49 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 29905.743348278025 50 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 33189.50246999955 51 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 36828.01526089957 52 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 40859.56101049569 53 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 45326.549616733195 54 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 50275.96585781884 55 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 55759.86322437042 56 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 61835.90884911595 57 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I -observable_confirmed_infected model1_China_Stage_I 68567.98916666959 58 sd_confirmed_infected_China_Stage_I model1_data_observable_confirmed_infected_China_Stage_I observable_confirmed_infected model1_China_Stage_II 4000.0 0 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II observable_confirmed_infected model1_China_Stage_II 4529.955981156226 1 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II observable_confirmed_infected model1_China_Stage_II 5183.766817497703 2 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II @@ -43,14 +24,6 @@ observable_confirmed_infected model1_China_Stage_II 10391.598465497258 11 sd_c observable_confirmed_infected model1_China_Stage_II 10720.151623671345 12 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II observable_confirmed_infected model1_China_Stage_II 11004.610020621114 13 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II observable_confirmed_infected model1_China_Stage_II 11249.610740921637 14 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II -observable_confirmed_infected model1_China_Stage_II 11459.672285606992 15 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II -observable_confirmed_infected model1_China_Stage_II 11639.061868635077 16 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II -observable_confirmed_infected model1_China_Stage_II 11791.719524289767 17 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II -observable_confirmed_infected model1_China_Stage_II 11921.221794098255 18 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II -observable_confirmed_infected model1_China_Stage_II 12030.772288564198 19 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II -observable_confirmed_infected model1_China_Stage_II 12123.210073419554 20 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II -observable_confirmed_infected model1_China_Stage_II 12201.029172878409 21 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II -observable_confirmed_infected model1_China_Stage_II 12266.404461231356 22 sd_confirmed_infected_China_Stage_II model1_data_observable_confirmed_infected_China_Stage_II observable_confirmed_infected model1_Hubei_Stage_I 339.9306290483404 15 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I observable_confirmed_infected model1_Hubei_Stage_I 443.2797002209989 16 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I observable_confirmed_infected model1_Hubei_Stage_I 575.8823423744068 17 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I @@ -61,25 +34,6 @@ observable_confirmed_infected model1_Hubei_Stage_I 1600.319314024311 21 sd_con observable_confirmed_infected model1_Hubei_Stage_I 2057.933024086237 22 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I observable_confirmed_infected model1_Hubei_Stage_I 2643.6936397547756 23 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I observable_confirmed_infected model1_Hubei_Stage_I 3393.349887929675 24 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 4352.593730170196 25 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 5579.8047107869825 26 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 7149.537984791677 27 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 9156.94884711046 28 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 11723.392573762732 29 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 15003.488011322157 30 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 19193.990577985784 31 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 24544.88395893585 32 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 31373.134555171426 33 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 40079.62210548101 34 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 51169.713641480994 35 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 65277.893016540984 36 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 83196.6424813204 37 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 105909.38134167925 38 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 134626.57973130292 39 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 170823.19604684066 40 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 216274.1242115494 41 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 273082.697433498 42 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I -observable_confirmed_infected model1_Hubei_Stage_I 343695.43638098857 43 sd_confirmed_infected_Hubei_Stage_I model1_data_observable_confirmed_infected_Hubei_Stage_I observable_confirmed_infected model1_Hubei_Stage_II 1600.0 0 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II observable_confirmed_infected model1_Hubei_Stage_II 2095.8511448870468 1 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II observable_confirmed_infected model1_Hubei_Stage_II 2905.487013487504 2 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II @@ -95,16 +49,6 @@ observable_confirmed_infected model1_Hubei_Stage_II 11954.455962712012 11 sd_c observable_confirmed_infected model1_Hubei_Stage_II 12664.148143198008 12 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II observable_confirmed_infected model1_Hubei_Stage_II 13298.96225437696 13 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II observable_confirmed_infected model1_Hubei_Stage_II 13863.45029444149 14 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II -observable_confirmed_infected model1_Hubei_Stage_II 14362.850318149167 15 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II -observable_confirmed_infected model1_Hubei_Stage_II 14802.709468093912 16 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II -observable_confirmed_infected model1_Hubei_Stage_II 15188.61753663063 17 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II -observable_confirmed_infected model1_Hubei_Stage_II 15526.02327020389 18 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II -observable_confirmed_infected model1_Hubei_Stage_II 15820.114419319407 19 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II -observable_confirmed_infected model1_Hubei_Stage_II 16075.743094330013 20 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II -observable_confirmed_infected model1_Hubei_Stage_II 16297.385313284769 21 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II -observable_confirmed_infected model1_Hubei_Stage_II 16489.124789776582 22 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II -observable_confirmed_infected model1_Hubei_Stage_II 16654.65382288976 23 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II -observable_confirmed_infected model1_Hubei_Stage_II 16797.28571110865 24 sd_confirmed_infected_Hubei_Stage_II model1_data_observable_confirmed_infected_Hubei_Stage_II observable_confirmed_infected model1_Wuhan_Stage_I 623.4894980636154 15 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I observable_confirmed_infected model1_Wuhan_Stage_I 729.5145100718623 16 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I observable_confirmed_infected model1_Wuhan_Stage_I 863.8288416489295 17 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I @@ -115,25 +59,6 @@ observable_confirmed_infected model1_Wuhan_Stage_I 1864.3495712770527 21 sd_co observable_confirmed_infected model1_Wuhan_Stage_I 2298.3423623079566 22 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I observable_confirmed_infected model1_Wuhan_Stage_I 2846.3334462080397 23 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I observable_confirmed_infected model1_Wuhan_Stage_I 3537.9890554868725 24 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 4410.592086206255 25 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 5510.934174793033 26 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 6897.640951977852 27 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 8644.00814811753 28 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 10841.42758864212 29 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 13603.474292999217 30 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 17070.700777461898 31 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 21416.141991943306 32 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 26851.450247184606 33 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 33633.453148678906 34 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 42070.75073767001 35 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 52529.72589265236 36 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 65439.06864887752 37 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 81291.63442739107 38 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 100642.27491158429 39 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 124100.26327833798 40 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 152315.28944679696 41 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 185956.6993148903 42 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I -observable_confirmed_infected model1_Wuhan_Stage_I 225686.74933692656 43 sd_confirmed_infected_Wuhan_Stage_I model1_data_observable_confirmed_infected_Wuhan_Stage_I observable_confirmed_infected model1_Wuhan_Stage_II 2000.0 0 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II observable_confirmed_infected model1_Wuhan_Stage_II 2182.387259854863 1 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II observable_confirmed_infected model1_Wuhan_Stage_II 2692.208036291985 2 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II @@ -149,16 +74,6 @@ observable_confirmed_infected model1_Wuhan_Stage_II 15278.999982177762 11 sd_c observable_confirmed_infected model1_Wuhan_Stage_II 17053.319255539798 12 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II observable_confirmed_infected model1_Wuhan_Stage_II 18835.417273867417 13 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II observable_confirmed_infected model1_Wuhan_Stage_II 20613.975248096172 14 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II -observable_confirmed_infected model1_Wuhan_Stage_II 22379.367775519644 15 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II -observable_confirmed_infected model1_Wuhan_Stage_II 24123.467344653847 16 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II -observable_confirmed_infected model1_Wuhan_Stage_II 25839.46980677376 17 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II -observable_confirmed_infected model1_Wuhan_Stage_II 27521.738403806954 18 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II -observable_confirmed_infected model1_Wuhan_Stage_II 29165.662805258216 19 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II -observable_confirmed_infected model1_Wuhan_Stage_II 30767.534172513682 20 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II -observable_confirmed_infected model1_Wuhan_Stage_II 32324.432448324344 21 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II -observable_confirmed_infected model1_Wuhan_Stage_II 33834.128381828545 22 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II -observable_confirmed_infected model1_Wuhan_Stage_II 35294.99092651035 23 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II -observable_confirmed_infected model1_Wuhan_Stage_II 36705.91051995219 24 sd_confirmed_infected_Wuhan_Stage_II model1_data_observable_confirmed_infected_Wuhan_Stage_II observable_confirmed_infected model1_Wuhan_Stage_III 36000.0 0 sd_confirmed_infected_Wuhan_Stage_III model1_data_observable_confirmed_infected_Wuhan_Stage_III observable_confirmed_infected model1_Wuhan_Stage_III 37698.832088516756 1 sd_confirmed_infected_Wuhan_Stage_III model1_data_observable_confirmed_infected_Wuhan_Stage_III observable_confirmed_infected model1_Wuhan_Stage_III 39423.646390452326 2 sd_confirmed_infected_Wuhan_Stage_III model1_data_observable_confirmed_infected_Wuhan_Stage_III @@ -166,5 +81,3 @@ observable_confirmed_infected model1_Wuhan_Stage_III 41031.43409166907 3 sd_co observable_confirmed_infected model1_Wuhan_Stage_III 42455.93798575727 4 sd_confirmed_infected_Wuhan_Stage_III model1_data_observable_confirmed_infected_Wuhan_Stage_III observable_confirmed_infected model1_Wuhan_Stage_III 43676.24018030864 5 sd_confirmed_infected_Wuhan_Stage_III model1_data_observable_confirmed_infected_Wuhan_Stage_III observable_confirmed_infected model1_Wuhan_Stage_III 44696.824044238994 6 sd_confirmed_infected_Wuhan_Stage_III model1_data_observable_confirmed_infected_Wuhan_Stage_III -observable_confirmed_infected model1_Wuhan_Stage_III 45535.17671905029 7 sd_confirmed_infected_Wuhan_Stage_III model1_data_observable_confirmed_infected_Wuhan_Stage_III -observable_confirmed_infected model1_Wuhan_Stage_III 46214.29041199827 8 sd_confirmed_infected_Wuhan_Stage_III model1_data_observable_confirmed_infected_Wuhan_Stage_III From af574f96cc9c3dfdfe421dc21bd2d9f57b813cb2 Mon Sep 17 00:00:00 2001 From: Matthias Koenig Date: Mon, 2 Nov 2026 10:41:06 +0100 Subject: [PATCH 08/18] Regenerate the simulation tables of Fujita, Sneyd, Zheng and Perelson The simulations of these four problems were not the model at the nominal parameters of the parameter table (Fujita_SciSignal2010, Sneyd_PNAS2002, Perelson_Science1996) or were the measurements (Zheng_PNAS2012, #181). They are simulated again at the nominal parameters, row by row in the order of the measurements; the simulations of Fujita_SciSignal2010 get the current condition ids of the measurements (condition_step_*), the table still named the former ones (model1_data*). --- .../Fujita_SciSignal2010/v1/simulations.tsv | 288 +++++++++--------- .../Perelson_Science1996/v1/simulations.tsv | 32 +- problems/Sneyd_PNAS2002/v1/simulations.tsv | 270 ++++++++-------- problems/Zheng_PNAS2012/v1/simulations.tsv | 120 ++++---- 4 files changed, 355 insertions(+), 355 deletions(-) diff --git a/problems/Fujita_SciSignal2010/v1/simulations.tsv b/problems/Fujita_SciSignal2010/v1/simulations.tsv index 3e7fba57..a69e94fc 100644 --- a/problems/Fujita_SciSignal2010/v1/simulations.tsv +++ b/problems/Fujita_SciSignal2010/v1/simulations.tsv @@ -1,145 +1,145 @@ observableId preequilibrationConditionId simulationConditionId simulation time observableParameters noiseParameters observableTransformation datasetId -pEGFR_tot model1_data1 0 0 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot -pEGFR_tot model1_data1 0.012441876 60 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot -pEGFR_tot model1_data1 0.0187120065 120 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot -pEGFR_tot model1_data1 0.0158168482 300 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot -pEGFR_tot model1_data1 0.0092928584 600 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot -pEGFR_tot model1_data1 0.0054601697 900 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot -pEGFR_tot model1_data1 0.0011396679 1800 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot -pEGFR_tot model1_data1 0.0001018308 3600 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot -pAkt_tot model1_data1 0 0 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot -pAkt_tot model1_data1 0.0355108621 60 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot -pAkt_tot model1_data1 0.1053412851 120 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot -pAkt_tot model1_data1 0.167356844 300 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot -pAkt_tot model1_data1 0.1359909071 600 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot -pAkt_tot model1_data1 0.1071886841 900 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot -pAkt_tot model1_data1 0.0605225951 1800 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot -pAkt_tot model1_data1 0.0276204035 3600 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot -pS6_tot model1_data1 0 0 scaling_pS6_tot 0.01 lin model1_data1_pS6_tot -pS6_tot model1_data1 5.78895984805857E-05 60 scaling_pS6_tot 0.01 lin model1_data1_pS6_tot -pS6_tot model1_data1 0.0009531255 120 scaling_pS6_tot 0.01 lin model1_data1_pS6_tot -pS6_tot model1_data1 0.0185497283 300 scaling_pS6_tot 0.01 lin model1_data1_pS6_tot -pS6_tot model1_data1 0.0927501675 600 scaling_pS6_tot 0.02 lin model1_data1_pS6_tot -pS6_tot model1_data1 0.1859892615 900 scaling_pS6_tot 0.02 lin model1_data1_pS6_tot -pS6_tot model1_data1 0.3842285889 1800 scaling_pS6_tot 0.035 lin model1_data1_pS6_tot -pS6_tot model1_data1 0.3671651134 3600 scaling_pS6_tot 0.035 lin model1_data1_pS6_tot -pEGFR_tot model1_data2 0 0 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot -pEGFR_tot model1_data2 0.0366295138 60 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot -pEGFR_tot model1_data2 0.0536154835 120 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot -pEGFR_tot model1_data2 0.0405530546 300 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot -pEGFR_tot model1_data2 0.0194943054 600 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot -pEGFR_tot model1_data2 0.0093857599 900 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot -pEGFR_tot model1_data2 0.001138775 1800 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot -pEGFR_tot model1_data2 0.0001393242 3600 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot -pAkt_tot model1_data2 0 0 scaling_pAkt_tot 0.01 lin model1_data2_pAkt_tot -pAkt_tot model1_data2 0.1010762041 60 scaling_pAkt_tot 0.01 lin model1_data2_pAkt_tot -pAkt_tot model1_data2 0.2711737267 120 scaling_pAkt_tot 0.03 lin model1_data2_pAkt_tot -pAkt_tot model1_data2 0.3607036934 300 scaling_pAkt_tot 0.01 lin model1_data2_pAkt_tot -pAkt_tot model1_data2 0.2663746843 600 scaling_pAkt_tot 0.02 lin model1_data2_pAkt_tot -pAkt_tot model1_data2 0.1952487389 900 scaling_pAkt_tot 0.02 lin model1_data2_pAkt_tot -pAkt_tot model1_data2 0.1006842668 1800 scaling_pAkt_tot 0.02 lin model1_data2_pAkt_tot -pAkt_tot model1_data2 0.0474357736 3600 scaling_pAkt_tot 0.01 lin model1_data2_pAkt_tot -pS6_tot model1_data2 0 0 scaling_pS6_tot 0.01 lin model1_data2_pS6_tot -pS6_tot model1_data2 0.0001694707 60 scaling_pS6_tot 0.01 lin model1_data2_pS6_tot -pS6_tot model1_data2 0.0026563935 120 scaling_pS6_tot 0.01 lin model1_data2_pS6_tot -pS6_tot model1_data2 0.045833771 300 scaling_pS6_tot 0.01 lin model1_data2_pS6_tot -pS6_tot model1_data2 0.209607514 600 scaling_pS6_tot 0.02 lin model1_data2_pS6_tot -pS6_tot model1_data2 0.4007484657 900 scaling_pS6_tot 0.03 lin model1_data2_pS6_tot -pS6_tot model1_data2 0.7556316274 1800 scaling_pS6_tot 0.1 lin model1_data2_pS6_tot -pS6_tot model1_data2 0.6612775029 3600 scaling_pS6_tot 0.025 lin model1_data2_pS6_tot -pEGFR_tot model1_data3 0 0 scaling_pEGFR_tot 0.01 lin model1_data3_pEGFR_tot -pEGFR_tot model1_data3 0.1144234645 60 scaling_pEGFR_tot 0.05 lin model1_data3_pEGFR_tot -pEGFR_tot model1_data3 0.1527713319 120 scaling_pEGFR_tot 0.025 lin model1_data3_pEGFR_tot -pEGFR_tot model1_data3 0.0790765114 300 scaling_pEGFR_tot 0.065 lin model1_data3_pEGFR_tot -pEGFR_tot model1_data3 0.0190585422 600 scaling_pEGFR_tot 0.025 lin model1_data3_pEGFR_tot -pEGFR_tot model1_data3 0.0047011183 900 scaling_pEGFR_tot 0.02 lin model1_data3_pEGFR_tot -pEGFR_tot model1_data3 0.0002765524 1800 scaling_pEGFR_tot 0.01 lin model1_data3_pEGFR_tot -pEGFR_tot model1_data3 0.0002160785 3600 scaling_pEGFR_tot 0.01 lin model1_data3_pEGFR_tot -pAkt_tot model1_data3 0 0 scaling_pAkt_tot 0.01 lin model1_data3_pAkt_tot -pAkt_tot model1_data3 0.2842569939 60 scaling_pAkt_tot 0.1 lin model1_data3_pAkt_tot -pAkt_tot model1_data3 0.5853547296 120 scaling_pAkt_tot 0.07 lin model1_data3_pAkt_tot -pAkt_tot model1_data3 0.5688099515 300 scaling_pAkt_tot 0.03 lin model1_data3_pAkt_tot -pAkt_tot model1_data3 0.3263752664 600 scaling_pAkt_tot 0.03 lin model1_data3_pAkt_tot -pAkt_tot model1_data3 0.1969268998 900 scaling_pAkt_tot 0.03 lin model1_data3_pAkt_tot -pAkt_tot model1_data3 0.1059534913 1800 scaling_pAkt_tot 0.02 lin model1_data3_pAkt_tot -pAkt_tot model1_data3 0.0547391878 3600 scaling_pAkt_tot 0.01 lin model1_data3_pAkt_tot -pS6_tot model1_data3 0 0 scaling_pS6_tot 0.01 lin model1_data3_pS6_tot -pS6_tot model1_data3 0.0005211244 60 scaling_pS6_tot 0.01 lin model1_data3_pS6_tot -pS6_tot model1_data3 0.007106942 120 scaling_pS6_tot 0.01 lin model1_data3_pS6_tot -pS6_tot model1_data3 0.0945320284 300 scaling_pS6_tot 0.01 lin model1_data3_pS6_tot -pS6_tot model1_data3 0.366852041 600 scaling_pS6_tot 0.01 lin model1_data3_pS6_tot -pS6_tot model1_data3 0.6350909024 900 scaling_pS6_tot 0.025 lin model1_data3_pS6_tot -pS6_tot model1_data3 0.9989387811 1800 scaling_pS6_tot 0.07 lin model1_data3_pS6_tot -pS6_tot model1_data3 0.7818807584 3600 scaling_pS6_tot 0.075 lin model1_data3_pS6_tot -pEGFR_tot model1_data4 0 0 scaling_pEGFR_tot 0.01 lin model1_data4_pEGFR_tot -pEGFR_tot model1_data4 0.287552912 60 scaling_pEGFR_tot 0.15 lin model1_data4_pEGFR_tot -pEGFR_tot model1_data4 0.3027758925 120 scaling_pEGFR_tot 0.12 lin model1_data4_pEGFR_tot -pEGFR_tot model1_data4 0.0587401979 300 scaling_pEGFR_tot 0.15 lin model1_data4_pEGFR_tot -pEGFR_tot model1_data4 0.002420217 600 scaling_pEGFR_tot 0.065 lin model1_data4_pEGFR_tot -pEGFR_tot model1_data4 0.0003476384 900 scaling_pEGFR_tot 0.03 lin model1_data4_pEGFR_tot -pEGFR_tot model1_data4 0.0002698926 1800 scaling_pEGFR_tot 0.02 lin model1_data4_pEGFR_tot -pEGFR_tot model1_data4 0.0002698889 3600 scaling_pEGFR_tot 0.01 lin model1_data4_pEGFR_tot -pAkt_tot model1_data4 0 0 scaling_pAkt_tot 0.01 lin model1_data4_pAkt_tot -pAkt_tot model1_data4 0.5758083456 60 scaling_pAkt_tot 0.05 lin model1_data4_pAkt_tot -pAkt_tot model1_data4 0.8186882204 120 scaling_pAkt_tot 0.05 lin model1_data4_pAkt_tot -pAkt_tot model1_data4 0.573010296 300 scaling_pAkt_tot 0.02 lin model1_data4_pAkt_tot -pAkt_tot model1_data4 0.1916147877 600 scaling_pAkt_tot 0.03 lin model1_data4_pAkt_tot -pAkt_tot model1_data4 0.1261732528 900 scaling_pAkt_tot 0.02 lin model1_data4_pAkt_tot -pAkt_tot model1_data4 0.0883994539 1800 scaling_pAkt_tot 0.02 lin model1_data4_pAkt_tot -pAkt_tot model1_data4 0.0471965113 3600 scaling_pAkt_tot 0.02 lin model1_data4_pAkt_tot -pS6_tot model1_data4 0 0 scaling_pS6_tot 0.01 lin model1_data4_pS6_tot -pS6_tot model1_data4 0.001284326 60 scaling_pS6_tot 0.01 lin model1_data4_pS6_tot -pS6_tot model1_data4 0.0137701475 120 scaling_pS6_tot 0.01 lin model1_data4_pS6_tot -pS6_tot model1_data4 0.1357831768 300 scaling_pS6_tot 0.01 lin model1_data4_pS6_tot -pS6_tot model1_data4 0.4317319675 600 scaling_pS6_tot 0.01 lin model1_data4_pS6_tot -pS6_tot model1_data4 0.6581119566 900 scaling_pS6_tot 0.035 lin model1_data4_pS6_tot -pS6_tot model1_data4 0.8936164338 1800 scaling_pS6_tot 0.08 lin model1_data4_pS6_tot -pS6_tot model1_data4 0.6665565723 3600 scaling_pS6_tot 0.08 lin model1_data4_pS6_tot -pEGFR_tot model1_data5 0 0 scaling_pEGFR_tot 0.01 lin model1_data5_pEGFR_tot -pEGFR_tot model1_data5 0.5650691808 60 scaling_pEGFR_tot 0.3 lin model1_data5_pEGFR_tot -pEGFR_tot model1_data5 0.342130677 120 scaling_pEGFR_tot 0.15 lin model1_data5_pEGFR_tot -pEGFR_tot model1_data5 0.0103850661 300 scaling_pEGFR_tot 0.15 lin model1_data5_pEGFR_tot -pEGFR_tot model1_data5 0.0003059582 600 scaling_pEGFR_tot 0.05 lin model1_data5_pEGFR_tot -pEGFR_tot model1_data5 0.0002956752 900 scaling_pEGFR_tot 0.02 lin model1_data5_pEGFR_tot -pEGFR_tot model1_data5 0.0002956661 1800 scaling_pEGFR_tot 0.02 lin model1_data5_pEGFR_tot -pEGFR_tot model1_data5 0.0002956661 3600 scaling_pEGFR_tot 0.01 lin model1_data5_pEGFR_tot -pAkt_tot model1_data5 0 0 scaling_pAkt_tot 0.01 lin model1_data5_pAkt_tot -pAkt_tot model1_data5 0.8436917241 60 scaling_pAkt_tot 0.1 lin model1_data5_pAkt_tot -pAkt_tot model1_data5 0.8842362231 120 scaling_pAkt_tot 0.025 lin model1_data5_pAkt_tot -pAkt_tot model1_data5 0.385937608 300 scaling_pAkt_tot 0.02 lin model1_data5_pAkt_tot -pAkt_tot model1_data5 0.1201352557 600 scaling_pAkt_tot 0.02 lin model1_data5_pAkt_tot -pAkt_tot model1_data5 0.1006147115 900 scaling_pAkt_tot 0.02 lin model1_data5_pAkt_tot -pAkt_tot model1_data5 0.072815494 1800 scaling_pAkt_tot 0.02 lin model1_data5_pAkt_tot -pAkt_tot model1_data5 0.0398738227 3600 scaling_pAkt_tot 0.01 lin model1_data5_pAkt_tot -pS6_tot model1_data5 0 0 scaling_pS6_tot 0.01 lin model1_data5_pS6_tot -pS6_tot model1_data5 0.0026774961 60 scaling_pS6_tot 0.01 lin model1_data5_pS6_tot -pS6_tot model1_data5 0.0210783997 120 scaling_pS6_tot 0.01 lin model1_data5_pS6_tot -pS6_tot model1_data5 0.1570157565 300 scaling_pS6_tot 0.01 lin model1_data5_pS6_tot -pS6_tot model1_data5 0.4117751573 600 scaling_pS6_tot 0.01 lin model1_data5_pS6_tot -pS6_tot model1_data5 0.5823970015 900 scaling_pS6_tot 0.02 lin model1_data5_pS6_tot -pS6_tot model1_data5 0.7468131737 1800 scaling_pS6_tot 0.04 lin model1_data5_pS6_tot -pS6_tot model1_data5 0.5504628923 3600 scaling_pS6_tot 0.08 lin model1_data5_pS6_tot -pEGFR_tot model1_data6 0 0 scaling_pEGFR_tot 0.01 lin model1_data6_pEGFR_tot -pEGFR_tot model1_data6 0.6597249554 60 scaling_pEGFR_tot 0.1 lin model1_data6_pEGFR_tot -pEGFR_tot model1_data6 0.2653113927 120 scaling_pEGFR_tot 0.1 lin model1_data6_pEGFR_tot -pEGFR_tot model1_data6 0.0045492092 300 scaling_pEGFR_tot 0.2 lin model1_data6_pEGFR_tot -pEGFR_tot model1_data6 0.0003058683 600 scaling_pEGFR_tot 0.05 lin model1_data6_pEGFR_tot -pEGFR_tot model1_data6 0.0003039615 900 scaling_pEGFR_tot 0.02 lin model1_data6_pEGFR_tot -pEGFR_tot model1_data6 0.0003039608 1800 scaling_pEGFR_tot 0.01 lin model1_data6_pEGFR_tot -pEGFR_tot model1_data6 0.0003039608 3600 scaling_pEGFR_tot 0.01 lin model1_data6_pEGFR_tot -pAkt_tot model1_data6 0 0 scaling_pAkt_tot 0.01 lin model1_data6_pAkt_tot -pAkt_tot model1_data6 0.9235457558 60 scaling_pAkt_tot 0.05 lin model1_data6_pAkt_tot -pAkt_tot model1_data6 0.8638192492 120 scaling_pAkt_tot 0.02 lin model1_data6_pAkt_tot -pAkt_tot model1_data6 0.3145472814 300 scaling_pAkt_tot 0.02 lin model1_data6_pAkt_tot -pAkt_tot model1_data6 0.1105777921 600 scaling_pAkt_tot 0.02 lin model1_data6_pAkt_tot -pAkt_tot model1_data6 0.0946003394 900 scaling_pAkt_tot 0.02 lin model1_data6_pAkt_tot -pAkt_tot model1_data6 0.0687085418 1800 scaling_pAkt_tot 0.02 lin model1_data6_pAkt_tot -pAkt_tot model1_data6 0.037964715 3600 scaling_pAkt_tot 0.01 lin model1_data6_pAkt_tot -pS6_tot model1_data6 0 0 scaling_pS6_tot 0.01 lin model1_data6_pS6_tot -pS6_tot model1_data6 0.0039799398 60 scaling_pS6_tot 0.01 lin model1_data6_pS6_tot -pS6_tot model1_data6 0.0254606658 120 scaling_pS6_tot 0.01 lin model1_data6_pS6_tot -pS6_tot model1_data6 0.1631478226 300 scaling_pS6_tot 0.01 lin model1_data6_pS6_tot -pS6_tot model1_data6 0.4009703836 600 scaling_pS6_tot 0.02 lin model1_data6_pS6_tot -pS6_tot model1_data6 0.5574326493 900 scaling_pS6_tot 0.03 lin model1_data6_pS6_tot -pS6_tot model1_data6 0.7058108511 1800 scaling_pS6_tot 0.035 lin model1_data6_pS6_tot -pS6_tot model1_data6 0.5194180219 3600 scaling_pS6_tot 0.07 lin model1_data6_pS6_tot +pEGFR_tot condition_step_00_1 0.0 0 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot +pEGFR_tot condition_step_00_1 0.012480366411715928 60 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot +pEGFR_tot condition_step_00_1 0.018707506977356403 120 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot +pEGFR_tot condition_step_00_1 0.015700849112639876 300 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot +pEGFR_tot condition_step_00_1 0.009127527788965412 600 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot +pEGFR_tot condition_step_00_1 0.005303097697048003 900 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot +pEGFR_tot condition_step_00_1 0.0010598594303809863 1800 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot +pEGFR_tot condition_step_00_1 7.418075976475617e-05 3600 scaling_pEGFR_tot 0.01 lin model1_data1_pEGFR_tot +pAkt_tot condition_step_00_1 0.0 0 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot +pAkt_tot condition_step_00_1 0.03571967362373573 60 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot +pAkt_tot condition_step_00_1 0.10589829483225664 120 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot +pAkt_tot condition_step_00_1 0.16811422033992746 300 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot +pAkt_tot condition_step_00_1 0.13609452528616278 600 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot +pAkt_tot condition_step_00_1 0.10689767414384724 900 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot +pAkt_tot condition_step_00_1 0.060261864880839976 1800 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot +pAkt_tot condition_step_00_1 0.02767496261959281 3600 scaling_pAkt_tot 0.01 lin model1_data1_pAkt_tot +pS6_tot condition_step_00_1 0.0 0 scaling_pS6_tot 0.01 lin model1_data1_pS6_tot +pS6_tot condition_step_00_1 5.829970865626934e-05 60 scaling_pS6_tot 0.01 lin model1_data1_pS6_tot +pS6_tot condition_step_00_1 0.000959335241884471 120 scaling_pS6_tot 0.01 lin model1_data1_pS6_tot +pS6_tot condition_step_00_1 0.018661438388824954 300 scaling_pS6_tot 0.01 lin model1_data1_pS6_tot +pS6_tot condition_step_00_1 0.09323749963797492 600 scaling_pS6_tot 0.02 lin model1_data1_pS6_tot +pS6_tot condition_step_00_1 0.18675201104234102 900 scaling_pS6_tot 0.02 lin model1_data1_pS6_tot +pS6_tot condition_step_00_1 0.38460454334751737 1800 scaling_pS6_tot 0.035 lin model1_data1_pS6_tot +pS6_tot condition_step_00_1 0.3669885968601952 3600 scaling_pS6_tot 0.035 lin model1_data1_pS6_tot +pEGFR_tot condition_step_00_3 0.0 0 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot +pEGFR_tot condition_step_00_3 0.03674519730805315 60 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot +pEGFR_tot condition_step_00_3 0.05360268676492265 120 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot +pEGFR_tot condition_step_00_3 0.040225367479225164 300 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot +pEGFR_tot condition_step_00_3 0.019099778836771934 600 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot +pEGFR_tot condition_step_00_3 0.009071367652217055 900 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot +pEGFR_tot condition_step_00_3 0.0010279633390261969 1800 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot +pEGFR_tot condition_step_00_3 8.79345905668471e-05 3600 scaling_pEGFR_tot 0.01 lin model1_data2_pEGFR_tot +pAkt_tot condition_step_00_3 0.0 0 scaling_pAkt_tot 0.01 lin model1_data2_pAkt_tot +pAkt_tot condition_step_00_3 0.10163779699937657 60 scaling_pAkt_tot 0.01 lin model1_data2_pAkt_tot +pAkt_tot condition_step_00_3 0.2724201993678682 120 scaling_pAkt_tot 0.03 lin model1_data2_pAkt_tot +pAkt_tot condition_step_00_3 0.36197640213320137 300 scaling_pAkt_tot 0.01 lin model1_data2_pAkt_tot +pAkt_tot condition_step_00_3 0.2663359423878288 600 scaling_pAkt_tot 0.02 lin model1_data2_pAkt_tot +pAkt_tot condition_step_00_3 0.1946397610070221 900 scaling_pAkt_tot 0.02 lin model1_data2_pAkt_tot +pAkt_tot condition_step_00_3 0.10058579103709434 1800 scaling_pAkt_tot 0.02 lin model1_data2_pAkt_tot +pAkt_tot condition_step_00_3 0.04764267428617345 3600 scaling_pAkt_tot 0.01 lin model1_data2_pAkt_tot +pS6_tot condition_step_00_3 0.0 0 scaling_pS6_tot 0.01 lin model1_data2_pS6_tot +pS6_tot condition_step_00_3 0.0001706435330767396 60 scaling_pS6_tot 0.01 lin model1_data2_pS6_tot +pS6_tot condition_step_00_3 0.0026727931701907223 120 scaling_pS6_tot 0.01 lin model1_data2_pS6_tot +pS6_tot condition_step_00_3 0.0460782248215312 300 scaling_pS6_tot 0.01 lin model1_data2_pS6_tot +pS6_tot condition_step_00_3 0.21054033554381676 600 scaling_pS6_tot 0.02 lin model1_data2_pS6_tot +pS6_tot condition_step_00_3 0.40210221323583173 900 scaling_pS6_tot 0.03 lin model1_data2_pS6_tot +pS6_tot condition_step_00_3 0.7563839705341021 1800 scaling_pS6_tot 0.1 lin model1_data2_pS6_tot +pS6_tot condition_step_00_3 0.6621824263573951 3600 scaling_pS6_tot 0.025 lin model1_data2_pS6_tot +pEGFR_tot condition_step_01_0 0.0 0 scaling_pEGFR_tot 0.01 lin model1_data3_pEGFR_tot +pEGFR_tot condition_step_01_0 0.1148111626001067 60 scaling_pEGFR_tot 0.05 lin model1_data3_pEGFR_tot +pEGFR_tot condition_step_01_0 0.15271982137513834 120 scaling_pEGFR_tot 0.025 lin model1_data3_pEGFR_tot +pEGFR_tot condition_step_01_0 0.07817219585967418 300 scaling_pEGFR_tot 0.065 lin model1_data3_pEGFR_tot +pEGFR_tot condition_step_01_0 0.018452865572308172 600 scaling_pEGFR_tot 0.025 lin model1_data3_pEGFR_tot +pEGFR_tot condition_step_01_0 0.004416559341996262 900 scaling_pEGFR_tot 0.02 lin model1_data3_pEGFR_tot +pEGFR_tot condition_step_01_0 0.00018665301129610926 1800 scaling_pEGFR_tot 0.01 lin model1_data3_pEGFR_tot +pEGFR_tot condition_step_01_0 0.00013185523984646038 3600 scaling_pEGFR_tot 0.01 lin model1_data3_pEGFR_tot +pAkt_tot condition_step_01_0 0.0 0 scaling_pAkt_tot 0.01 lin model1_data3_pAkt_tot +pAkt_tot condition_step_01_0 0.28556263029157786 60 scaling_pAkt_tot 0.1 lin model1_data3_pAkt_tot +pAkt_tot condition_step_01_0 0.5871858562106247 120 scaling_pAkt_tot 0.07 lin model1_data3_pAkt_tot +pAkt_tot condition_step_01_0 0.5697990776354793 300 scaling_pAkt_tot 0.03 lin model1_data3_pAkt_tot +pAkt_tot condition_step_01_0 0.3255369327733615 600 scaling_pAkt_tot 0.03 lin model1_data3_pAkt_tot +pAkt_tot condition_step_01_0 0.19622609563116897 900 scaling_pAkt_tot 0.03 lin model1_data3_pAkt_tot +pAkt_tot condition_step_01_0 0.10636030480500712 1800 scaling_pAkt_tot 0.02 lin model1_data3_pAkt_tot +pAkt_tot condition_step_01_0 0.05474517710127448 3600 scaling_pAkt_tot 0.01 lin model1_data3_pAkt_tot +pS6_tot condition_step_01_0 0.0 0 scaling_pS6_tot 0.01 lin model1_data3_pS6_tot +pS6_tot condition_step_01_0 0.000524475262481269 60 scaling_pS6_tot 0.01 lin model1_data3_pS6_tot +pS6_tot condition_step_01_0 0.007143844432165558 120 scaling_pS6_tot 0.01 lin model1_data3_pS6_tot +pS6_tot condition_step_01_0 0.09491069144153012 300 scaling_pS6_tot 0.01 lin model1_data3_pS6_tot +pS6_tot condition_step_01_0 0.36794452835273667 600 scaling_pS6_tot 0.01 lin model1_data3_pS6_tot +pS6_tot condition_step_01_0 0.6363468304512627 900 scaling_pS6_tot 0.025 lin model1_data3_pS6_tot +pS6_tot condition_step_01_0 0.9999176306937233 1800 scaling_pS6_tot 0.07 lin model1_data3_pS6_tot +pS6_tot condition_step_01_0 0.7837469282313485 3600 scaling_pS6_tot 0.075 lin model1_data3_pS6_tot +pEGFR_tot condition_step_03_0 0.0 0 scaling_pEGFR_tot 0.01 lin model1_data4_pEGFR_tot +pEGFR_tot condition_step_03_0 0.2886782406856652 60 scaling_pEGFR_tot 0.15 lin model1_data4_pEGFR_tot +pEGFR_tot condition_step_03_0 0.30242804281267716 120 scaling_pEGFR_tot 0.12 lin model1_data4_pEGFR_tot +pEGFR_tot condition_step_03_0 0.05715699506779033 300 scaling_pEGFR_tot 0.15 lin model1_data4_pEGFR_tot +pEGFR_tot condition_step_03_0 0.0021515820456298215 600 scaling_pEGFR_tot 0.065 lin model1_data4_pEGFR_tot +pEGFR_tot condition_step_03_0 0.0002331126286492285 900 scaling_pEGFR_tot 0.03 lin model1_data4_pEGFR_tot +pEGFR_tot condition_step_03_0 0.00016512523251489418 1800 scaling_pEGFR_tot 0.02 lin model1_data4_pEGFR_tot +pEGFR_tot condition_step_03_0 0.00016512250714135668 3600 scaling_pEGFR_tot 0.01 lin model1_data4_pEGFR_tot +pAkt_tot condition_step_03_0 0.0 0 scaling_pAkt_tot 0.01 lin model1_data4_pAkt_tot +pAkt_tot condition_step_03_0 0.5775196213931701 60 scaling_pAkt_tot 0.05 lin model1_data4_pAkt_tot +pAkt_tot condition_step_03_0 0.8200665352991877 120 scaling_pAkt_tot 0.05 lin model1_data4_pAkt_tot +pAkt_tot condition_step_03_0 0.5723434671145193 300 scaling_pAkt_tot 0.02 lin model1_data4_pAkt_tot +pAkt_tot condition_step_03_0 0.19045378872267194 600 scaling_pAkt_tot 0.03 lin model1_data4_pAkt_tot +pAkt_tot condition_step_03_0 0.1261136825380296 900 scaling_pAkt_tot 0.02 lin model1_data4_pAkt_tot +pAkt_tot condition_step_03_0 0.08839189817721442 1800 scaling_pAkt_tot 0.02 lin model1_data4_pAkt_tot +pAkt_tot condition_step_03_0 0.04655742558075636 3600 scaling_pAkt_tot 0.02 lin model1_data4_pAkt_tot +pS6_tot condition_step_03_0 0.0 0 scaling_pS6_tot 0.01 lin model1_data4_pS6_tot +pS6_tot condition_step_03_0 0.0012913696656530827 60 scaling_pS6_tot 0.01 lin model1_data4_pS6_tot +pS6_tot condition_step_03_0 0.013823815051844856 120 scaling_pS6_tot 0.01 lin model1_data4_pS6_tot +pS6_tot condition_step_03_0 0.13614814190917518 300 scaling_pS6_tot 0.01 lin model1_data4_pS6_tot +pS6_tot condition_step_03_0 0.43222600720052035 600 scaling_pS6_tot 0.01 lin model1_data4_pS6_tot +pS6_tot condition_step_03_0 0.6582843946021298 900 scaling_pS6_tot 0.035 lin model1_data4_pS6_tot +pS6_tot condition_step_03_0 0.8936402752356469 1800 scaling_pS6_tot 0.08 lin model1_data4_pS6_tot +pS6_tot condition_step_03_0 0.6658815407462093 3600 scaling_pS6_tot 0.08 lin model1_data4_pS6_tot +pEGFR_tot condition_step_10_0 0.0 0 scaling_pEGFR_tot 0.01 lin model1_data5_pEGFR_tot +pEGFR_tot condition_step_10_0 0.5676704383138094 60 scaling_pEGFR_tot 0.3 lin model1_data5_pEGFR_tot +pEGFR_tot condition_step_10_0 0.33967350986051936 120 scaling_pEGFR_tot 0.15 lin model1_data5_pEGFR_tot +pEGFR_tot condition_step_10_0 0.009400619471596115 300 scaling_pEGFR_tot 0.15 lin model1_data5_pEGFR_tot +pEGFR_tot condition_step_10_0 0.00018845153139739382 600 scaling_pEGFR_tot 0.05 lin model1_data5_pEGFR_tot +pEGFR_tot condition_step_10_0 0.00018112668995170903 900 scaling_pEGFR_tot 0.02 lin model1_data5_pEGFR_tot +pEGFR_tot condition_step_10_0 0.00018112197383655625 1800 scaling_pEGFR_tot 0.02 lin model1_data5_pEGFR_tot +pEGFR_tot condition_step_10_0 0.00018112197383884256 3600 scaling_pEGFR_tot 0.01 lin model1_data5_pEGFR_tot +pAkt_tot condition_step_10_0 0.0 0 scaling_pAkt_tot 0.01 lin model1_data5_pAkt_tot +pAkt_tot condition_step_10_0 0.844867156245151 60 scaling_pAkt_tot 0.1 lin model1_data5_pAkt_tot +pAkt_tot condition_step_10_0 0.8849068422667229 120 scaling_pAkt_tot 0.025 lin model1_data5_pAkt_tot +pAkt_tot condition_step_10_0 0.3827420055965652 300 scaling_pAkt_tot 0.02 lin model1_data5_pAkt_tot +pAkt_tot condition_step_10_0 0.11977132624725684 600 scaling_pAkt_tot 0.02 lin model1_data5_pAkt_tot +pAkt_tot condition_step_10_0 0.10030494068131744 900 scaling_pAkt_tot 0.02 lin model1_data5_pAkt_tot +pAkt_tot condition_step_10_0 0.07230920319529495 1800 scaling_pAkt_tot 0.02 lin model1_data5_pAkt_tot +pAkt_tot condition_step_10_0 0.03872221513723069 3600 scaling_pAkt_tot 0.01 lin model1_data5_pAkt_tot +pS6_tot condition_step_10_0 0.0 0 scaling_pS6_tot 0.01 lin model1_data5_pS6_tot +pS6_tot condition_step_10_0 0.002688760168924027 60 scaling_pS6_tot 0.01 lin model1_data5_pS6_tot +pS6_tot condition_step_10_0 0.021137630242921993 120 scaling_pS6_tot 0.01 lin model1_data5_pS6_tot +pS6_tot condition_step_10_0 0.15721132436693347 300 scaling_pS6_tot 0.01 lin model1_data5_pS6_tot +pS6_tot condition_step_10_0 0.41147869920730656 600 scaling_pS6_tot 0.01 lin model1_data5_pS6_tot +pS6_tot condition_step_10_0 0.5816658865220378 900 scaling_pS6_tot 0.02 lin model1_data5_pS6_tot +pS6_tot condition_step_10_0 0.7454041663064397 1800 scaling_pS6_tot 0.04 lin model1_data5_pS6_tot +pS6_tot condition_step_10_0 0.5470002311288191 3600 scaling_pS6_tot 0.08 lin model1_data5_pS6_tot +pEGFR_tot condition_step_30_0 0.0 0 scaling_pEGFR_tot 0.01 lin model1_data6_pEGFR_tot +pEGFR_tot condition_step_30_0 0.6616674233641531 60 scaling_pEGFR_tot 0.1 lin model1_data6_pEGFR_tot +pEGFR_tot condition_step_30_0 0.2608340355735843 120 scaling_pEGFR_tot 0.1 lin model1_data6_pEGFR_tot +pEGFR_tot condition_step_30_0 0.004016150335610498 300 scaling_pEGFR_tot 0.2 lin model1_data6_pEGFR_tot +pEGFR_tot condition_step_30_0 0.0001876717298537902 600 scaling_pEGFR_tot 0.05 lin model1_data6_pEGFR_tot +pEGFR_tot condition_step_30_0 0.00018627933745822008 900 scaling_pEGFR_tot 0.02 lin model1_data6_pEGFR_tot +pEGFR_tot condition_step_30_0 0.0001862789481140307 1800 scaling_pEGFR_tot 0.01 lin model1_data6_pEGFR_tot +pEGFR_tot condition_step_30_0 0.0001862789481162307 3600 scaling_pEGFR_tot 0.01 lin model1_data6_pEGFR_tot +pAkt_tot condition_step_30_0 0.0 0 scaling_pAkt_tot 0.01 lin model1_data6_pAkt_tot +pAkt_tot condition_step_30_0 0.9243819721360509 60 scaling_pAkt_tot 0.05 lin model1_data6_pAkt_tot +pAkt_tot condition_step_30_0 0.8637204571184451 120 scaling_pAkt_tot 0.02 lin model1_data6_pAkt_tot +pAkt_tot condition_step_30_0 0.31206244424114904 300 scaling_pAkt_tot 0.02 lin model1_data6_pAkt_tot +pAkt_tot condition_step_30_0 0.11027573547753863 600 scaling_pAkt_tot 0.02 lin model1_data6_pAkt_tot +pAkt_tot condition_step_30_0 0.0942146449555446 900 scaling_pAkt_tot 0.02 lin model1_data6_pAkt_tot +pAkt_tot condition_step_30_0 0.06808725487689075 1800 scaling_pAkt_tot 0.02 lin model1_data6_pAkt_tot +pAkt_tot condition_step_30_0 0.03667874310909305 3600 scaling_pAkt_tot 0.01 lin model1_data6_pAkt_tot +pS6_tot condition_step_30_0 0.0 0 scaling_pS6_tot 0.01 lin model1_data6_pS6_tot +pS6_tot condition_step_30_0 0.00399438146767243 60 scaling_pS6_tot 0.01 lin model1_data6_pS6_tot +pS6_tot condition_step_30_0 0.025521860562909136 120 scaling_pS6_tot 0.01 lin model1_data6_pS6_tot +pS6_tot condition_step_30_0 0.16323537546041705 300 scaling_pS6_tot 0.01 lin model1_data6_pS6_tot +pS6_tot condition_step_30_0 0.40058494805328826 600 scaling_pS6_tot 0.02 lin model1_data6_pS6_tot +pS6_tot condition_step_30_0 0.5566922316075167 900 scaling_pS6_tot 0.03 lin model1_data6_pS6_tot +pS6_tot condition_step_30_0 0.7042997888996284 1800 scaling_pS6_tot 0.035 lin model1_data6_pS6_tot +pS6_tot condition_step_30_0 0.5153837860708252 3600 scaling_pS6_tot 0.07 lin model1_data6_pS6_tot diff --git a/problems/Perelson_Science1996/v1/simulations.tsv b/problems/Perelson_Science1996/v1/simulations.tsv index e68cd7a8..d517155b 100644 --- a/problems/Perelson_Science1996/v1/simulations.tsv +++ b/problems/Perelson_Science1996/v1/simulations.tsv @@ -1,17 +1,17 @@ observableId preequilibrationConditionId simulationConditionId simulation time observableParameters noiseParameters observableTransformation noiseDistribution -task0_model0_perelson1_V condition1 1860000 0 1 log10 normal -task0_model0_perelson1_V condition1 1979067.19116451 0.105 1 log10 normal -task0_model0_perelson1_V condition1 2036200.82349015 0.169 1 log10 normal -task0_model0_perelson1_V condition1 2110605.53393048 0.282 1 log10 normal -task0_model0_perelson1_V condition1 2170386.58687064 0.492 1 log10 normal -task0_model0_perelson1_V condition1 2133014.95269381 0.757 1 log10 normal -task0_model0_perelson1_V condition1 2007892.04532337 1.029 1 log10 normal -task0_model0_perelson1_V condition1 1866529.16992371 1.253 1 log10 normal -task0_model0_perelson1_V condition1 1667316.74545722 1.533 1 log10 normal -task0_model0_perelson1_V condition1 1508578.7515073 1.75 1 log10 normal -task0_model0_perelson1_V condition1 1304224.95565882 2.038 1 log10 normal -task0_model0_perelson1_V condition1 745147.675932322 3.013 1 log10 normal -task0_model0_perelson1_V condition1 404793.605719818 3.987 1 log10 normal -task0_model0_perelson1_V condition1 214718.67877913 4.968 1 log10 normal -task0_model0_perelson1_V condition1 111378.497695453 5.972 1 log10 normal -task0_model0_perelson1_V condition1 57731.3816378903 6.973 1 log10 normal +task0_model0_perelson1_V condition1 1860000.0 0 1 log10 normal +task0_model0_perelson1_V condition1 1859281.260373289 0.105 1 log10 normal +task0_model0_perelson1_V condition1 1857219.5663062893 0.169 1 log10 normal +task0_model0_perelson1_V condition1 1848638.4703770746 0.282 1 log10 normal +task0_model0_perelson1_V condition1 1812120.1993839357 0.492 1 log10 normal +task0_model0_perelson1_V condition1 1728263.03640854 0.757 1 log10 normal +task0_model0_perelson1_V condition1 1608815.6791831392 1.029 1 log10 normal +task0_model0_perelson1_V condition1 1495225.9952656787 1.253 1 log10 normal +task0_model0_perelson1_V condition1 1345380.619535358 1.533 1 log10 normal +task0_model0_perelson1_V condition1 1229342.8058591573 1.75 1 log10 normal +task0_model0_perelson1_V condition1 1081458.8464350805 2.038 1 log10 normal +task0_model0_perelson1_V condition1 671706.4854312212 3.013 1 log10 normal +task0_model0_perelson1_V condition1 405639.170141612 3.987 1 log10 normal +task0_model0_perelson1_V condition1 241982.66835108443 4.968 1 log10 normal +task0_model0_perelson1_V condition1 142258.69595914753 5.972 1 log10 normal +task0_model0_perelson1_V condition1 83708.79730856746 6.973 1 log10 normal diff --git a/problems/Sneyd_PNAS2002/v1/simulations.tsv b/problems/Sneyd_PNAS2002/v1/simulations.tsv index 79f93e26..c544774f 100644 --- a/problems/Sneyd_PNAS2002/v1/simulations.tsv +++ b/problems/Sneyd_PNAS2002/v1/simulations.tsv @@ -1,136 +1,136 @@ observableId preequilibrationConditionId simulationConditionId simulation time observableParameters noiseParameters experimentName replicateId useData datasetId -open_probability Ca_dose_response__1 0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.121894546523477 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.060595553808957 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.029927743627544 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.016966738060837 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.010950865102333 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.007889935956002 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.006208795803398 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.00522799579081 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.004628767636314 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.004249773944653 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.004003778690146 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.003840913784634 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.003731366618313 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__1 0.003656677446066 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability -open_probability Ca_dose_response__2 0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.215678142415106 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.163761426885113 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.102217430277337 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.067586627905053 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.04794160008511 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.036246811779277 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.028958334975966 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.024232259124856 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.021063632687336 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.018879468210961 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.017339073625141 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.016231996423034 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.015423728256309 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__2 0.014825697161151 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability -open_probability Ca_dose_response__3 0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.261822224490706 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.243214273475024 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.165452868159381 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.114719898954169 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.083539032274217 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.063834828747858 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.05094768692273 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.042247387361814 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.036207087269719 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.031910713745902 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.028790694098721 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.02648455706322 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.02475422564705 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__3 0.023439256475873 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability -open_probability Ca_dose_response__4 0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.256421638211286 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.244049602963311 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.157567043658913 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.102262809203666 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.069922277704295 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.050522181826376 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.038429952523923 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.030609970408551 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.02538346521554 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.021788797300514 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.019255261437112 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.017432484512352 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.016098394582075 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__4 0.015108032006753 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability -open_probability Ca_dose_response__5 0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.209956782778307 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.171912802309381 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.091220598909618 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.049356198337219 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.029091174010564 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.018788457182827 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.013206212177869 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.00999556752894 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.008051163083109 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.006822135884553 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.006018101069233 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.005477684256377 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.00510679643968 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__5 0.004848188811793 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability -open_probability Ca_dose_response__6 0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.227499981859172 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.198777336708334 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.113821705477149 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.065924070834603 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.040993686128187 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.027509842618924 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.019820431596413 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.01520884723014 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.012317850790703 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.01043614698577 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.009172808913423 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.008303103892224 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.007692317288332 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability Ca_dose_response__6 0.007256603205075 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability -open_probability IP3_dose_response__3 0 0 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.261822224490706 0.07 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.243214273475024 0.14 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.165452868159381 0.21 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.114719898954169 0.28 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.083539032274217 0.35 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.063834828747858 0.42 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.05094768692273 0.49 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.042247387361814 0.56 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.036207087269719 0.63 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.031910713745902 0.7 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.028790694098721 0.77 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.02648455706322 0.84 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.02475422564705 0.91 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__3 0.023439256475873 0.98 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability -open_probability IP3_dose_response__1 0 0 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.027513716704193 0.07 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.099307231943202 0.14 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.130202341702809 0.21 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.122285020707863 0.28 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.101303258567466 0.35 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.080710796039658 0.42 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.064352347846962 0.49 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.052281488561359 0.56 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.043582860721469 0.63 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.037326744571104 0.7 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.032792900371487 0.77 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.029469364593807 0.84 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.027002522397321 0.91 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__1 0.025149254710067 0.98 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability -open_probability IP3_dose_response__2 0 0 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.093722639361853 0.07 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.190123164511576 0.14 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.17175740987611 0.21 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.129468384266291 0.28 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.095441173133522 0.35 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.072142226375651 0.42 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.05661310471373 0.49 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.046149533503429 0.56 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.038946262778702 0.63 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.033872579795436 0.7 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.03022216500319 0.77 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.027546187017617 0.84 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.025552684613735 0.91 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability -open_probability IP3_dose_response__2 0.024047022739061 0.98 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability Ca_dose_response__1 0.0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.12189460915173836 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.060595600126932575 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.029927747690899155 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.016966738184186755 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.010950865016580569 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.00788993497917522 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.006208793678784985 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.005227993716165281 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.004628765872477415 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.004249772507970916 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.004003775559654114 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.003840909956038643 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.0037313630748248743 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__1 0.0036566744017752094 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__1_open_probability +open_probability Ca_dose_response__2 0.0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.2156783939883289 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.1637614891619114 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.10221748009569213 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.06758663128322337 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.04794160095064292 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.036246811756707686 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.02895833452169648 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.024232254002516274 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.02106362574537538 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.018879461030966715 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.01733906706469798 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.016231990638657746 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.015423722602488974 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__2 0.014825686310077113 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__2_open_probability +open_probability Ca_dose_response__3 0.0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.2618225025935463 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.24321444278325874 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.1654529503356218 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.11471991304692097 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.08353903388654797 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.06383482928345324 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.05094768699926752 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.042247385778450136 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.036207085125253335 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.03191071148988078 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.02879068724118109 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.02648454614961488 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.024754214888953425 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__3 0.023439245751075994 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__3_open_probability +open_probability Ca_dose_response__4 0.0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.2564225786499537 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.24404998252690585 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.15756717917046456 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.10226283989199549 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.0699222820429615 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.05052218348324393 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.03842995291531829 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.03060996965934795 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.025383461604882435 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.021788793547349455 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.019255257861479427 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.01743247528761651 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.016098378686154265 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__4 0.015108016535030223 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__4_open_probability +open_probability Ca_dose_response__5 0.0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.2275002081564284 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.19877746111460623 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.1138218314343141 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.0659241477807292 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.040993698203914665 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.0275098457918434 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.01982043274444511 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.015208845183712684 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.012317836689914867 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.010436121509725577 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.009172784861281806 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.008303081521988904 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.00769229724467487 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__5 0.007256568219058782 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__5_open_probability +open_probability Ca_dose_response__6 0.0 0 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.2099567704552069 0.07 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.17191308059998966 0.14 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.09122077319433003 0.21 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.0493564717388131 0.28 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.029091210122601234 0.35 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.01878846041346061 0.42 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.013206212136100176 0.49 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.009995563352867354 0.56 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.00805114833223848 0.63 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.0068221220281195856 0.7 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.006018088811625641 0.77 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.005477673976590076 0.84 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.005106787345759894 0.91 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability Ca_dose_response__6 0.004848173616728174 0.98 0.022665679544462 Ca^{2+} response 1 TRUE Ca_dose_response__6_open_probability +open_probability IP3_dose_response__1 0.0 0 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.027513671263886486 0.07 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.09930732855326843 0.14 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.13020255378899842 0.21 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.12228519139539264 0.28 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.1013033617652879 0.35 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.080710851428122 0.42 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.06435237669477711 0.49 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.05228152505352381 0.56 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.0435828851196742 0.63 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.03732675856308595 0.7 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.032792907790655974 0.77 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.029469368282055822 0.84 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.027002524036249757 0.91 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__1 0.025149255284731435 0.98 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__3_open_probability +open_probability IP3_dose_response__2 0.0 0 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.09372223757657416 0.07 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.19012287720246318 0.14 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.17175743779599476 0.21 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.12946841801585346 0.28 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.09544099532473556 0.35 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.07214208802881261 0.42 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.05661304979415705 0.49 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.04614951797350116 0.56 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.038946261606131266 0.63 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.033872583449151325 0.7 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.03022216922898188 0.77 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.02754619052835453 0.84 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.025552686467794135 0.91 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__2 0.024047023208516195 0.98 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__1_open_probability +open_probability IP3_dose_response__3 0.0 0 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.2618225025935463 0.07 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.24321444278325874 0.14 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.1654529503356218 0.21 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.11471991304692097 0.28 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.08353903388654797 0.35 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.06383482928345324 0.42 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.05094768699926752 0.49 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.042247385778450136 0.56 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.036207085125253335 0.63 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.03191071148988078 0.7 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.02879068724118109 0.77 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.02648454614961488 0.84 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.024754214888953425 0.91 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability +open_probability IP3_dose_response__3 0.023439245751075994 0.98 0.022665679544462 IP_3 response 1 TRUE IP3_dose_response__2_open_probability diff --git a/problems/Zheng_PNAS2012/v1/simulations.tsv b/problems/Zheng_PNAS2012/v1/simulations.tsv index c80d6c8f..931d3073 100644 --- a/problems/Zheng_PNAS2012/v1/simulations.tsv +++ b/problems/Zheng_PNAS2012/v1/simulations.tsv @@ -1,61 +1,61 @@ observableId preequilibrationConditionId simulationConditionId simulation time observableParameters noiseParameters -observable_K27me0K36me0 preequilibration simCondition 0.00431 0 0.002339466852399 -observable_K27me0K36me0 preequilibration simCondition 0.00093 5 0.002339466852399 -observable_K27me0K36me0 preequilibration simCondition 0.00051 10 0.002339466852399 -observable_K27me0K36me0 preequilibration simCondition 0.00054 25 0.002339466852399 -observable_K27me0K36me1 preequilibration simCondition 0.00789 0 0.002339466852399 -observable_K27me0K36me1 preequilibration simCondition 0.00499 5 0.002339466852399 -observable_K27me0K36me1 preequilibration simCondition 0.00308 10 0.002339466852399 -observable_K27me0K36me1 preequilibration simCondition 0.00131 25 0.002339466852399 -observable_K27me1K36me0 preequilibration simCondition 0.00216 0 0.002339466852399 -observable_K27me1K36me0 preequilibration simCondition 0.00159 5 0.002339466852399 -observable_K27me1K36me0 preequilibration simCondition 0.00123 10 0.002339466852399 -observable_K27me1K36me0 preequilibration simCondition 0.00091 25 0.002339466852399 -observable_K27me0K36me2 preequilibration simCondition 0.16777 0 0.002339466852399 -observable_K27me0K36me2 preequilibration simCondition 0.14469 5 0.002339466852399 -observable_K27me0K36me2 preequilibration simCondition 0.1214 10 0.002339466852399 -observable_K27me0K36me2 preequilibration simCondition 0.05789 25 0.002339466852399 -observable_K27me1K36me1 preequilibration simCondition 0.00908 0 0.002339466852399 -observable_K27me1K36me1 preequilibration simCondition 0.00724 5 0.002339466852399 -observable_K27me1K36me1 preequilibration simCondition 0.00619 10 0.002339466852399 -observable_K27me1K36me1 preequilibration simCondition 0.00323 25 0.002339466852399 -observable_K27me2K36me0 preequilibration simCondition 0.00623 0 0.002339466852399 -observable_K27me2K36me0 preequilibration simCondition 0.00684 5 0.002339466852399 -observable_K27me2K36me0 preequilibration simCondition 0.00623 10 0.002339466852399 -observable_K27me2K36me0 preequilibration simCondition 0.00609 25 0.002339466852399 -observable_K27me0K36me3 preequilibration simCondition 0.04922 0 0.002339466852399 -observable_K27me0K36me3 preequilibration simCondition 0.05666 5 0.002339466852399 -observable_K27me0K36me3 preequilibration simCondition 0.06029 10 0.002339466852399 -observable_K27me0K36me3 preequilibration simCondition 0.05922 25 0.002339466852399 -observable_K27me1K36me2 preequilibration simCondition 0.59615 0 0.002339466852399 -observable_K27me1K36me2 preequilibration simCondition 0.60752 5 0.002339466852399 -observable_K27me1K36me2 preequilibration simCondition 0.61758 10 0.002339466852399 -observable_K27me1K36me2 preequilibration simCondition 0.6578 25 0.002339466852399 -observable_K27me2K36me1 preequilibration simCondition 0.01362 0 0.002339466852399 -observable_K27me2K36me1 preequilibration simCondition 0.01367 5 0.002339466852399 -observable_K27me2K36me1 preequilibration simCondition 0.01347 10 0.002339466852399 -observable_K27me2K36me1 preequilibration simCondition 0.0131 25 0.002339466852399 -observable_K27me3K36me0 preequilibration simCondition 0.00285 0 0.002339466852399 -observable_K27me3K36me0 preequilibration simCondition 0.00309 5 0.002339466852399 -observable_K27me3K36me0 preequilibration simCondition 0.00365 10 0.002339466852399 -observable_K27me3K36me0 preequilibration simCondition 0.00398 25 0.002339466852399 -observable_K27me1K36me3 preequilibration simCondition 0.10672 0 0.002339466852399 -observable_K27me1K36me3 preequilibration simCondition 0.11679 5 0.002339466852399 -observable_K27me1K36me3 preequilibration simCondition 0.1198 10 0.002339466852399 -observable_K27me1K36me3 preequilibration simCondition 0.14836 25 0.002339466852399 -observable_K27me2K36me2 preequilibration simCondition 0.02539 0 0.002339466852399 -observable_K27me2K36me2 preequilibration simCondition 0.02709 5 0.002339466852399 -observable_K27me2K36me2 preequilibration simCondition 0.03716 10 0.002339466852399 -observable_K27me2K36me2 preequilibration simCondition 0.03508 25 0.002339466852399 -observable_K27me3K36me1 preequilibration simCondition 0.00223 0 0.002339466852399 -observable_K27me3K36me1 preequilibration simCondition 0.00263 5 0.002339466852399 -observable_K27me3K36me1 preequilibration simCondition 0.00255 10 0.002339466852399 -observable_K27me3K36me1 preequilibration simCondition 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0.006174738426115336 10 0.002339466852399 +observable_K27me2K36me3 preequilibration simCondition 0.0066841923473993144 25 0.002339466852399 +observable_K27me3K36me2 preequilibration simCondition 0.0013814742836317387 0 0.002339466852399 +observable_K27me3K36me2 preequilibration simCondition 0.0015916887827325057 5 0.002339466852399 +observable_K27me3K36me2 preequilibration simCondition 0.0016686890329107155 10 0.002339466852399 +observable_K27me3K36me2 preequilibration simCondition 0.001283207474590104 25 0.002339466852399 From 8c3b3dead148ef9d3471eba16bc15cfe74bec819 Mon Sep 17 00:00:00 2001 From: Matthias Koenig Date: Mon, 2 Nov 2026 12:30:44 +0100 Subject: [PATCH 09/18] Regenerate the simulation table of Chen_MSB2009 The table differed from the model at the nominal parameters in 5 of 120 simulations by up to 0.28 % (AKT_PP of model1_data4 at t = 1950: 17.7718 instead of 17.7228). The simulations are the ones of AMICI 1.2.0 (simulate_petab, absolute and relative tolerance 1e-16 and 1e-12), which sbmlsim reproduces to 4e-6; their log-likelihood is -130591.753. --- problems/Chen_MSB2009/v1/simulations.tsv | 240 +++++++++++------------ 1 file changed, 120 insertions(+), 120 deletions(-) diff --git a/problems/Chen_MSB2009/v1/simulations.tsv b/problems/Chen_MSB2009/v1/simulations.tsv index c0fa78f0..3db042ee 100644 --- a/problems/Chen_MSB2009/v1/simulations.tsv +++ b/problems/Chen_MSB2009/v1/simulations.tsv @@ -1,121 +1,121 @@ observableId preequilibrationConditionId simulationConditionId simulation time observableParameters noiseParameters datasetId -ERB_B1_P_tot model1_data1 8.88387621921431E-13 1800 EGFR_t 0.1 model1_data1_ERB_B1_P_tot -ERB_B1_P_tot model1_data1 43.5718043105709 1950 EGFR_t 7.4 model1_data1_ERB_B1_P_tot -ERB_B1_P_tot model1_data1 52.1145985039882 2100 EGFR_t 6.8 model1_data1_ERB_B1_P_tot -ERB_B1_P_tot model1_data1 55.7486613742705 2250 EGFR_t 8.9 model1_data1_ERB_B1_P_tot -ERB_B1_P_tot model1_data1 57.8091974066766 2400 EGFR_t 4.7 model1_data1_ERB_B1_P_tot -ERB_B1_P_tot model1_data1 60.0885250712489 2700 EGFR_t 4.7 model1_data1_ERB_B1_P_tot -ERB_B1_P_tot model1_data1 62.5599123936139 3600 EGFR_t 2.5 model1_data1_ERB_B1_P_tot -ERB_B1_P_tot model1_data1 63.3375379783163 4500 EGFR_t 2.4 model1_data1_ERB_B1_P_tot -ERB_B1_P_tot model1_data1 63.6475120605309 5400 EGFR_t 13.1 model1_data1_ERB_B1_P_tot -ERB_B1_P_tot model1_data1 63.657723259226 9000 EGFR_t 13.1 model1_data1_ERB_B1_P_tot -ERK_PP model1_data1 2.52423312491575E-11 1800 ERK_t 0.1 model1_data1_ERK_PP -ERK_PP model1_data1 69.3855801236379 1950 ERK_t 0.46 model1_data1_ERK_PP -ERK_PP model1_data1 69.0806370662617 2100 ERK_t 1.9 model1_data1_ERK_PP -ERK_PP model1_data1 67.3562315614362 2250 ERK_t 1.5 model1_data1_ERK_PP -ERK_PP model1_data1 65.4726536397062 2400 ERK_t 1.2 model1_data1_ERK_PP -ERK_PP model1_data1 61.7105809137709 2700 ERK_t 3.1 model1_data1_ERK_PP -ERK_PP model1_data1 52.1279030059724 3600 ERK_t 2.8 model1_data1_ERK_PP -ERK_PP model1_data1 45.3081820299199 4500 ERK_t 2.9 model1_data1_ERK_PP -ERK_PP model1_data1 40.6661974926109 5400 ERK_t 3.7 model1_data1_ERK_PP -ERK_PP model1_data1 33.747577355144 9000 ERK_t 1.5 model1_data1_ERK_PP -AKT_PP model1_data1 5.29862853957213E-52 1800 AKT_t 0.1 model1_data1_AKT_PP -AKT_PP model1_data1 46.9128475628656 1950 AKT_t 0.1 model1_data1_AKT_PP -AKT_PP model1_data1 49.3644152750207 2100 AKT_t 0.36 model1_data1_AKT_PP -AKT_PP model1_data1 50.0708103111029 2250 AKT_t 0.69 model1_data1_AKT_PP -AKT_PP model1_data1 50.2875849549111 2400 AKT_t 2.3 model1_data1_AKT_PP -AKT_PP model1_data1 50.2843567254821 2700 AKT_t 2.1 model1_data1_AKT_PP -AKT_PP model1_data1 49.8232571479622 3600 AKT_t 1.5 model1_data1_AKT_PP -AKT_PP model1_data1 49.3838023306466 4500 AKT_t 3.5 model1_data1_AKT_PP -AKT_PP model1_data1 48.9860095764 5400 AKT_t 2.3 model1_data1_AKT_PP -AKT_PP model1_data1 47.5709579212551 9000 AKT_t 7.4 model1_data1_AKT_PP -ERB_B1_P_tot model1_data2 3.36363329625249E-17 1800 EGFR_t 0.1 model1_data2_ERB_B1_P_tot -ERB_B1_P_tot model1_data2 0.000528876163967 1950 EGFR_t 0.1 model1_data2_ERB_B1_P_tot -ERB_B1_P_tot model1_data2 0.000498882602886 2100 EGFR_t 0.1 model1_data2_ERB_B1_P_tot -ERB_B1_P_tot model1_data2 0.000466345782826 2250 EGFR_t 0.1 model1_data2_ERB_B1_P_tot -ERB_B1_P_tot model1_data2 0.00045884216035 2400 EGFR_t 0.1 model1_data2_ERB_B1_P_tot -ERB_B1_P_tot model1_data2 0.000465416745889 2700 EGFR_t 0.1 model1_data2_ERB_B1_P_tot -ERB_B1_P_tot model1_data2 0.000500321469747 3600 EGFR_t 0.1 model1_data2_ERB_B1_P_tot -ERB_B1_P_tot model1_data2 0.000534193225367 4500 EGFR_t 0.1 model1_data2_ERB_B1_P_tot -ERB_B1_P_tot model1_data2 0.000566166733081 5400 EGFR_t 0.1 model1_data2_ERB_B1_P_tot -ERB_B1_P_tot model1_data2 0.000675558959884 9000 EGFR_t 0.1 model1_data2_ERB_B1_P_tot -ERK_PP model1_data2 5.00383145707368E-154 1800 ERK_t 0.1 model1_data2_ERK_PP -ERK_PP model1_data2 24.262444854452 1950 ERK_t 3.4 model1_data2_ERK_PP -ERK_PP model1_data2 31.9517037134021 2100 ERK_t 6.8 model1_data2_ERK_PP -ERK_PP model1_data2 32.6244225748506 2250 ERK_t 1.9 model1_data2_ERK_PP -ERK_PP model1_data2 31.7682524046738 2400 ERK_t 4 model1_data2_ERK_PP -ERK_PP model1_data2 28.6759620505581 2700 ERK_t 4.3 model1_data2_ERK_PP -ERK_PP model1_data2 21.2022643602703 3600 ERK_t 2.7 model1_data2_ERK_PP -ERK_PP model1_data2 17.8628027432288 4500 ERK_t 1.2 model1_data2_ERK_PP -ERK_PP model1_data2 16.5580541395666 5400 ERK_t 0.94 model1_data2_ERK_PP -ERK_PP model1_data2 15.8181136826053 9000 ERK_t 2.3 model1_data2_ERK_PP -AKT_PP model1_data2 5.08139567615343E-76 1800 AKT_t 0.1 model1_data2_AKT_PP -AKT_PP model1_data2 56.035388130668 1950 AKT_t 4.7 model1_data2_AKT_PP -AKT_PP model1_data2 69.4880500801865 2100 AKT_t 3.2 model1_data2_AKT_PP -AKT_PP model1_data2 73.4248062028344 2250 AKT_t 4.6 model1_data2_AKT_PP -AKT_PP model1_data2 74.2439422655282 2400 AKT_t 3.2 model1_data2_AKT_PP -AKT_PP model1_data2 73.7648085482688 2700 AKT_t 2.9 model1_data2_AKT_PP -AKT_PP model1_data2 69.4182443713225 3600 AKT_t 2.8 model1_data2_AKT_PP -AKT_PP model1_data2 64.8617703174951 4500 AKT_t 2.6 model1_data2_AKT_PP -AKT_PP model1_data2 61.2321756306954 5400 AKT_t 1.8 model1_data2_AKT_PP -AKT_PP model1_data2 52.0027923944557 9000 AKT_t 2.3 model1_data2_AKT_PP -ERB_B1_P_tot model1_data3 1.76775554783494E-15 1800 EGFR_t 0.1 model1_data3_ERB_B1_P_tot -ERB_B1_P_tot model1_data3 0.036418682306522 1950 EGFR_t 0.1 model1_data3_ERB_B1_P_tot -ERB_B1_P_tot model1_data3 0.038141189649734 2100 EGFR_t 0.1 model1_data3_ERB_B1_P_tot -ERB_B1_P_tot model1_data3 0.037788264080809 2250 EGFR_t 0.1 model1_data3_ERB_B1_P_tot -ERB_B1_P_tot model1_data3 0.037891012706382 2400 EGFR_t 0.1 model1_data3_ERB_B1_P_tot -ERB_B1_P_tot model1_data3 0.038998169813646 2700 EGFR_t 0.1 model1_data3_ERB_B1_P_tot -ERB_B1_P_tot model1_data3 0.04291752099434 3600 EGFR_t 0.1 model1_data3_ERB_B1_P_tot -ERB_B1_P_tot model1_data3 0.045741898484001 4500 EGFR_t 0.1 model1_data3_ERB_B1_P_tot -ERB_B1_P_tot model1_data3 0.047697723787962 5400 EGFR_t 0.1 model1_data3_ERB_B1_P_tot -ERB_B1_P_tot model1_data3 0.051140032777417 9000 EGFR_t 0.1 model1_data3_ERB_B1_P_tot -ERK_PP model1_data3 4.05689429468581E-22 1800 ERK_t 0.1 model1_data3_ERK_PP -ERK_PP model1_data3 19.1283412548992 1950 ERK_t 3.4 model1_data3_ERK_PP -ERK_PP model1_data3 29.0262991158362 2100 ERK_t 6.8 model1_data3_ERK_PP -ERK_PP model1_data3 29.3496850233104 2250 ERK_t 1.9 model1_data3_ERK_PP -ERK_PP model1_data3 28.3669494838099 2400 ERK_t 4 model1_data3_ERK_PP -ERK_PP model1_data3 25.2944505933136 2700 ERK_t 4.3 model1_data3_ERK_PP -ERK_PP model1_data3 18.3269400330114 3600 ERK_t 2.7 model1_data3_ERK_PP -ERK_PP model1_data3 15.5414077429536 4500 ERK_t 1.2 model1_data3_ERK_PP -ERK_PP model1_data3 14.5840346873173 5400 ERK_t 0.94 model1_data3_ERK_PP -ERK_PP model1_data3 14.1306270404719 9000 ERK_t 2.3 model1_data3_ERK_PP -AKT_PP model1_data3 8.49071717018033E-63 1800 AKT_t 0.1 model1_data3_AKT_PP -AKT_PP model1_data3 0.001239915893444 1950 AKT_t 0.98 model1_data3_AKT_PP -AKT_PP model1_data3 33.7142067464579 2100 AKT_t 11.1 model1_data3_AKT_PP -AKT_PP model1_data3 43.9127707791346 2250 AKT_t 5.6 model1_data3_AKT_PP -AKT_PP model1_data3 46.3374858930585 2400 AKT_t 3.1 model1_data3_AKT_PP -AKT_PP model1_data3 48.2386429805479 2700 AKT_t 4.5 model1_data3_AKT_PP -AKT_PP model1_data3 49.1448803968136 3600 AKT_t 5.1 model1_data3_AKT_PP -AKT_PP model1_data3 49.1112264321989 4500 AKT_t 14.1 model1_data3_AKT_PP -AKT_PP model1_data3 48.9883910876766 5400 AKT_t 9.7 model1_data3_AKT_PP -AKT_PP model1_data3 48.435645908212 9000 AKT_t 5.6 model1_data3_AKT_PP -ERB_B1_P_tot model1_data4 6.7277291085564E-19 1800 EGFR_t 0.1 model1_data4_ERB_B1_P_tot -ERB_B1_P_tot model1_data4 2.92689710183634E-05 1950 EGFR_t 0.1 model1_data4_ERB_B1_P_tot -ERB_B1_P_tot model1_data4 3.28578777243557E-05 2100 EGFR_t 0.1 model1_data4_ERB_B1_P_tot -ERB_B1_P_tot model1_data4 3.10986842926543E-05 2250 EGFR_t 0.1 model1_data4_ERB_B1_P_tot -ERB_B1_P_tot model1_data4 3.02366245604384E-05 2400 EGFR_t 0.1 model1_data4_ERB_B1_P_tot -ERB_B1_P_tot model1_data4 3.00182582173071E-05 2700 EGFR_t 0.1 model1_data4_ERB_B1_P_tot -ERB_B1_P_tot model1_data4 3.08359536201293E-05 3600 EGFR_t 0.1 model1_data4_ERB_B1_P_tot -ERB_B1_P_tot model1_data4 3.20124467350121E-05 4500 EGFR_t 0.1 model1_data4_ERB_B1_P_tot -ERB_B1_P_tot model1_data4 3.34324595680437E-05 5400 EGFR_t 0.1 model1_data4_ERB_B1_P_tot -ERB_B1_P_tot model1_data4 4.00121953425809E-05 9000 EGFR_t 0.1 model1_data4_ERB_B1_P_tot -ERK_PP model1_data4 8.64657840082615E-161 1800 ERK_t 0.1 model1_data4_ERK_PP -ERK_PP model1_data4 11.8508078706432 1950 ERK_t 4.3 model1_data4_ERK_PP -ERK_PP model1_data4 24.0162664665207 2100 ERK_t 1.1 model1_data4_ERK_PP -ERK_PP model1_data4 22.909537519056 2250 ERK_t 8.2 model1_data4_ERK_PP -ERK_PP model1_data4 21.5370745078285 2400 ERK_t 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2.2 model1_data4_ERK_PP +AKT_PP model1_data4 1.9582007003665646e-79 1800 AKT_t 0.1 model1_data4_AKT_PP +AKT_PP model1_data4 17.722759617608318 1950 AKT_t 0.1 model1_data4_AKT_PP +AKT_PP model1_data4 49.77856316753892 2100 AKT_t 4.6 model1_data4_AKT_PP +AKT_PP model1_data4 54.386515373285384 2250 AKT_t 7.7 model1_data4_AKT_PP +AKT_PP model1_data4 57.41423151545343 2400 AKT_t 3.6 model1_data4_AKT_PP +AKT_PP model1_data4 61.3017545460959 2700 AKT_t 3.6 model1_data4_AKT_PP +AKT_PP model1_data4 66.69639690291952 3600 AKT_t 1.6 model1_data4_AKT_PP +AKT_PP model1_data4 68.58345923886567 4500 AKT_t 13.1 model1_data4_AKT_PP +AKT_PP model1_data4 69.01652510128231 5400 AKT_t 13.1 model1_data4_AKT_PP +AKT_PP model1_data4 65.47238257549402 9000 AKT_t 12.1 model1_data4_AKT_PP From ff31a81fd9d9fac25cc7b44c9c9713e7a0629037 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 15:05:44 +0000 Subject: [PATCH 10/18] Remove the problems fixed by #343 from KNOWN_FAILURES Chen_MSB2009, Fujita_SciSignal2010, Perelson_Science1996, Sneyd_PNAS2002 and Zheng_PNAS2012 reproduce their regenerated simulation tables. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/simulate.yml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index 3a463550..9b06b166 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -45,13 +45,8 @@ jobs: # reported, but do not fail the job. Remove a problem from this list # once its simulation table has been fixed. KNOWN_FAILURES: >- - Chen_MSB2009 - Fujita_SciSignal2010 Liu_IFACPapersOnLine2025 - Perelson_Science1996 Raia_CancerResearch2011 - Sneyd_PNAS2002 - Zheng_PNAS2012 run: | failed=() known_failed=() From c47917f6208f90adce405e9c024a4f7c7b076158 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 15:08:34 +0000 Subject: [PATCH 11/18] Add the simulation tables of Okuonghae and Oliveira The measurement tables with the measurements replaced by the AMICI simulations at the nominal parameters (simulate_petab, rtol 1e-12, atol 1e-16), which change by less than 1e-8 (relative) from rtol 1e-10. Log-likelihoods: Okuonghae_ChaosSolitonsFractals2020 -470490.606, Oliveira_NatCommun2021 -9623396.411. Co-Authored-By: Claude Opus 5.5 --- .../v1/simulations.tsv | 93 ++++++++++++++ .../Oliveira_NatCommun2021/v1/simulations.tsv | 121 ++++++++++++++++++ 2 files changed, 214 insertions(+) create mode 100644 problems/Okuonghae_ChaosSolitonsFractals2020/v1/simulations.tsv create mode 100644 problems/Oliveira_NatCommun2021/v1/simulations.tsv diff --git a/problems/Okuonghae_ChaosSolitonsFractals2020/v1/simulations.tsv b/problems/Okuonghae_ChaosSolitonsFractals2020/v1/simulations.tsv new file mode 100644 index 00000000..098442e4 --- /dev/null +++ b/problems/Okuonghae_ChaosSolitonsFractals2020/v1/simulations.tsv @@ -0,0 +1,93 @@ +observableId simulationConditionId simulation time noiseParameters datasetId +observable_ActiveCases cond1 1.0 0 sd_ActiveCases id1 +observable_ActiveCases cond1 3.720654398600152 1 sd_ActiveCases id1 +observable_ActiveCases cond1 6.351796012907393 2 sd_ActiveCases id1 +observable_ActiveCases cond1 8.931214467167091 3 sd_ActiveCases id1 +observable_ActiveCases cond1 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sd_Cumulative id1 +observable_Cumulative cond1 95.69381723730635 18 sd_Cumulative id1 +observable_Cumulative cond1 105.31091690503321 19 sd_Cumulative id1 +observable_Cumulative cond1 115.64913716665708 20 sd_Cumulative id1 +observable_Cumulative cond1 126.76314889259648 21 sd_Cumulative id1 +observable_Cumulative cond1 138.71167015624744 22 sd_Cumulative id1 +observable_Cumulative cond1 151.5577780764682 23 sd_Cumulative id1 +observable_Cumulative cond1 165.3692424184475 24 sd_Cumulative id1 +observable_Cumulative cond1 180.21888283179572 25 sd_Cumulative id1 +observable_Cumulative cond1 196.1849516465987 26 sd_Cumulative id1 +observable_Cumulative cond1 213.35154424971074 27 sd_Cumulative id1 +observable_Cumulative cond1 231.80903918479498 28 sd_Cumulative id1 +observable_Cumulative cond1 251.65457022370035 29 sd_Cumulative id1 +observable_Cumulative cond1 272.99253281758416 30 sd_Cumulative id1 +observable_Cumulative cond1 295.9351274813509 31 sd_Cumulative id1 +observable_Cumulative cond1 320.60294279465154 32 sd_Cumulative id1 +observable_Cumulative cond1 347.12558094585387 33 sd_Cumulative id1 +observable_Cumulative cond1 375.64232886498513 34 sd_Cumulative id1 +observable_Cumulative cond1 406.3028782292644 35 sd_Cumulative id1 +observable_Cumulative cond1 439.26809781225217 36 sd_Cumulative id1 +observable_Cumulative cond1 474.7108619030989 37 sd_Cumulative id1 +observable_Cumulative cond1 512.8169387150994 38 sd_Cumulative id1 +observable_Cumulative cond1 553.785942966447 39 sd_Cumulative id1 +observable_Cumulative cond1 597.8323571123105 40 sd_Cumulative id1 +observable_Cumulative cond1 645.1866258874693 41 sd_Cumulative id1 +observable_Cumulative cond1 696.0963292100806 42 sd_Cumulative id1 +observable_Cumulative cond1 750.827438680458 43 sd_Cumulative id1 +observable_Cumulative cond1 809.6656633141107 44 sd_Cumulative id1 +observable_Cumulative cond1 872.9178903471569 45 sd_Cumulative id1 +observable_Cumulative cond1 940.9137273902172 46 sd_Cumulative id1 +observable_Cumulative cond1 1014.0071523955352 47 sd_Cumulative id1 diff --git a/problems/Oliveira_NatCommun2021/v1/simulations.tsv b/problems/Oliveira_NatCommun2021/v1/simulations.tsv new file mode 100644 index 00000000..fdddb9f1 --- /dev/null +++ b/problems/Oliveira_NatCommun2021/v1/simulations.tsv @@ -0,0 +1,121 @@ +observableId simulationConditionId simulation time noiseParameters datasetId +cumulative_cases interior 4.016823843999999 0 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.0 0 sd_cumulative_deaths dataset_ +cumulative_cases interior 4.232910613332748 1 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.0018464674802871025 1 sd_cumulative_deaths dataset_ +cumulative_cases interior 4.720373390831938 2 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.006844705972374982 2 sd_cumulative_deaths dataset_ +cumulative_cases interior 5.407971148239844 3 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.014484301565801425 3 sd_cumulative_deaths dataset_ +cumulative_cases interior 6.268988875481741 4 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.024530264139292924 4 sd_cumulative_deaths dataset_ +cumulative_cases interior 7.301012212500675 5 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.03692767942870849 5 sd_cumulative_deaths dataset_ +cumulative_cases interior 8.51586127128967 6 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.05175107826893596 6 sd_cumulative_deaths dataset_ +cumulative_cases interior 9.934744287239655 7 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.06917820778330565 7 sd_cumulative_deaths dataset_ +cumulative_cases interior 11.586119799970927 8 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.08947766440500492 8 sd_cumulative_deaths dataset_ +cumulative_cases interior 13.504994613978068 9 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.11300490241069465 9 sd_cumulative_deaths dataset_ +cumulative_cases interior 15.733017275159906 10 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.14020376750961808 10 sd_cumulative_deaths dataset_ +cumulative_cases interior 18.31905058678254 11 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.17161210026185553 11 sd_cumulative_deaths dataset_ +cumulative_cases interior 21.32007336262766 12 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.20787071149288947 12 sd_cumulative_deaths dataset_ +cumulative_cases interior 24.80234846433119 13 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.2497354609036835 13 sd_cumulative_deaths dataset_ +cumulative_cases interior 28.842840629143765 14 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.2980924304263014 14 sd_cumulative_deaths dataset_ +cumulative_cases interior 33.530893803503716 15 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.35397635850191944 15 sd_cumulative_deaths dataset_ +cumulative_cases interior 38.97019398767455 16 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.4185926342802002 16 sd_cumulative_deaths dataset_ +cumulative_cases interior 45.281055290777665 17 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.4933432654642824 17 sd_cumulative_deaths dataset_ +cumulative_cases interior 52.603076779374405 18 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.5798573435918158 18 sd_cumulative_deaths dataset_ +cumulative_cases interior 61.09822728400052 19 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.6800266442273317 19 sd_cumulative_deaths dataset_ +cumulative_cases interior 70.95442549274934 20 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.7960471227460275 20 sd_cumulative_deaths dataset_ +cumulative_cases interior 82.38969392845338 21 sd_cumulative_cases dataset_ +cumulative_deaths interior 0.9304672031511093 21 sd_cumulative_deaths dataset_ +cumulative_cases interior 95.65697823046153 22 sd_cumulative_cases dataset_ +cumulative_deaths interior 1.086243912175769 22 sd_cumulative_deaths dataset_ +cumulative_cases interior 111.04973789973741 23 sd_cumulative_cases dataset_ +cumulative_deaths interior 1.2668080876780563 23 sd_cumulative_deaths dataset_ +cumulative_cases interior 128.90843164105328 24 sd_cumulative_cases dataset_ +cumulative_deaths interior 1.4761400928496606 24 sd_cumulative_deaths dataset_ +cumulative_cases interior 149.62804011326682 25 sd_cumulative_cases dataset_ +cumulative_deaths interior 1.718857701615158 25 sd_cumulative_deaths dataset_ +cumulative_cases interior 173.66679162401041 26 sd_cumulative_cases dataset_ +cumulative_deaths interior 2.000318090053818 26 sd_cumulative_deaths dataset_ +cumulative_cases interior 201.55628262682112 27 sd_cumulative_cases dataset_ +cumulative_deaths interior 2.326736180411507 27 sd_cumulative_deaths dataset_ +cumulative_cases interior 233.91321535794964 28 sd_cumulative_cases dataset_ +cumulative_deaths interior 2.7053219451636936 28 sd_cumulative_deaths dataset_ +cumulative_cases interior 271.45301013076346 29 sd_cumulative_cases dataset_ +cumulative_deaths interior 3.1444396953851945 29 sd_cumulative_deaths dataset_ +cumulative_cases interior 311.7610503112301 30 sd_cumulative_cases dataset_ +cumulative_deaths interior 3.6535348128084797 30 sd_cumulative_deaths dataset_ +cumulative_cases interior 352.60732884364944 31 sd_cumulative_cases dataset_ +cumulative_deaths interior 4.240785823963287 31 sd_cumulative_deaths dataset_ +cumulative_cases interior 394.74255570668157 32 sd_cumulative_cases dataset_ +cumulative_deaths interior 4.911677758895421 32 sd_cumulative_deaths dataset_ +cumulative_cases interior 438.6417319626975 33 sd_cumulative_cases dataset_ +cumulative_deaths interior 5.670089032939824 33 sd_cumulative_deaths dataset_ +cumulative_cases interior 484.6280292281781 34 sd_cumulative_cases dataset_ +cumulative_deaths interior 6.519086691025875 34 sd_cumulative_deaths dataset_ +cumulative_cases interior 532.9424427202579 35 sd_cumulative_cases dataset_ 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sd_cumulative_deaths dataset_ +cumulative_cases interior 951.573940945607 42 sd_cumulative_cases dataset_ +cumulative_deaths interior 16.90874402547355 42 sd_cumulative_deaths dataset_ +cumulative_cases interior 1024.9845129497658 43 sd_cumulative_cases dataset_ +cumulative_deaths interior 18.706435757827837 43 sd_cumulative_deaths dataset_ +cumulative_cases interior 1102.371446877746 44 sd_cumulative_cases dataset_ +cumulative_deaths interior 20.628564193359036 44 sd_cumulative_deaths dataset_ +cumulative_cases interior 1183.9501348530016 45 sd_cumulative_cases dataset_ +cumulative_deaths interior 22.680124721622295 45 sd_cumulative_deaths dataset_ +cumulative_cases interior 1269.9473610503549 46 sd_cumulative_cases dataset_ +cumulative_deaths interior 24.866489354294696 46 sd_cumulative_deaths dataset_ +cumulative_cases interior 1360.6019872403692 47 sd_cumulative_cases dataset_ +cumulative_deaths interior 27.1934218950077 47 sd_cumulative_deaths dataset_ +cumulative_cases interior 1456.165640137862 48 sd_cumulative_cases dataset_ +cumulative_deaths interior 29.667093709884874 48 sd_cumulative_deaths dataset_ +cumulative_cases interior 1556.9034150852556 49 sd_cumulative_cases dataset_ +cumulative_deaths interior 32.294100296295454 49 sd_cumulative_deaths dataset_ +cumulative_cases interior 1663.0946052712445 50 sd_cumulative_cases dataset_ +cumulative_deaths interior 35.08147880232098 50 sd_cumulative_deaths dataset_ +cumulative_cases interior 1775.0334622789826 51 sd_cumulative_cases dataset_ +cumulative_deaths interior 38.036726610746506 51 sd_cumulative_deaths dataset_ +cumulative_cases interior 1893.029992183682 52 sd_cumulative_cases dataset_ +cumulative_deaths interior 41.1678210857592 52 sd_cumulative_deaths dataset_ +cumulative_cases interior 2017.4107903660342 53 sd_cumulative_cases dataset_ +cumulative_deaths interior 44.48324056464232 53 sd_cumulative_deaths dataset_ +cumulative_cases interior 2148.51991777127 54 sd_cumulative_cases dataset_ +cumulative_deaths interior 47.99198667154463 54 sd_cumulative_deaths dataset_ +cumulative_cases interior 2286.7198210773486 55 sd_cumulative_cases dataset_ +cumulative_deaths interior 51.703608025525845 55 sd_cumulative_deaths dataset_ +cumulative_cases interior 2432.392299102784 56 sd_cumulative_cases dataset_ +cumulative_deaths interior 55.628225412321804 56 sd_cumulative_deaths dataset_ +cumulative_cases interior 2585.9395177806973 57 sd_cumulative_cases dataset_ +cumulative_deaths interior 59.776558489567 57 sd_cumulative_deaths dataset_ +cumulative_cases interior 2747.785076066559 58 sd_cumulative_cases dataset_ +cumulative_deaths interior 64.15995409662534 58 sd_cumulative_deaths dataset_ +cumulative_cases interior 2918.3751251481613 59 sd_cumulative_cases dataset_ +cumulative_deaths interior 68.7904162402748 59 sd_cumulative_deaths dataset_ From 5eab3947f0c2ef65668028792c477b027b720891 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 15:10:54 +0000 Subject: [PATCH 12/18] Regenerate the simulation table of Raia_CancerResearch2011 The table had the column names simulationCondition and preequilibrationCondition instead of simulationConditionId and preequilibrationConditionId, observable IDs without the prefix observable_ of the observable table, empty noiseParameters, and lacked the columns observableTransformation, noiseDistribution and datasetId of the measurement table. Its values agree with the new ones to 5e-6 (relative). The new table is the measurement table with the measurements replaced by the AMICI simulations at the nominal parameters (simulate_petab, rtol 1e-12, atol 1e-16), which change by about 1e-9 (relative) from rtol 1e-10; log-likelihood -346.090. Raia_CancerResearch2011 is removed from KNOWN_FAILURES. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/simulate.yml | 1 - .../v1/simulations.tsv | 412 +++++++++--------- 2 files changed, 206 insertions(+), 207 deletions(-) diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index 9b06b166..61c94d45 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -46,7 +46,6 @@ jobs: # once its simulation table has been fixed. KNOWN_FAILURES: >- Liu_IFACPapersOnLine2025 - Raia_CancerResearch2011 run: | failed=() known_failed=() diff --git a/problems/Raia_CancerResearch2011/v1/simulations.tsv b/problems/Raia_CancerResearch2011/v1/simulations.tsv index d1c95790..badcb5bb 100644 --- a/problems/Raia_CancerResearch2011/v1/simulations.tsv +++ b/problems/Raia_CancerResearch2011/v1/simulations.tsv @@ -1,206 +1,206 @@ -observableId preequilibrationCondition simulationCondition simulation time observableParameters noiseParameters -RecSurf model1_data1 1.3 0 -RecSurf model1_data1 1.427926242 10 -RecSurf model1_data1 1.4319383562 20 -RecSurf model1_data1 1.432064218 30 -RecSurf model1_data1 1.4320683867 60 -RecSurf model1_data1 1.4320684562 90 -RecSurf model1_data1 1.4320684584 120 -IL13_cell model1_data1 0 0 -IL13_cell model1_data1 0 15 -IL13_cell model1_data1 0 30 -IL13_cell model1_data1 0 60 -IL13_cell model1_data1 0 90 -IL13_cell model1_data1 0 120 -SOCS3mRNA model1_data1 0 0 -SOCS3mRNA model1_data1 0 20 -SOCS3mRNA model1_data1 0 30 -SOCS3mRNA model1_data1 0 40 -SOCS3mRNA model1_data1 0 60 -SOCS3mRNA model1_data1 0 80 -SOCS3mRNA model1_data1 0 90 -SOCS3mRNA model1_data1 0 100 -SOCS3mRNA model1_data1 0 120 -CD274mRNA model1_data1 0 30 -CD274mRNA model1_data1 0 60 -CD274mRNA model1_data1 0 90 -CD274mRNA model1_data1 0 120 -SOCS3 model1_data1 0 0 -SOCS3 model1_data1 0 20 -SOCS3 model1_data1 0 40 -SOCS3 model1_data1 0 60 -SOCS3 model1_data1 0 90 -SOCS3 model1_data1 0 120 -IL13_cell model1_data2 3.7024348123 5 -IL13_cell model1_data2 5.1026012059 10 -IL13_cell model1_data2 5.6061765227 15 -IL13_cell model1_data2 5.7871881096 20 -IL13_cell model1_data2 5.8672215586 30 -IL13_cell model1_data2 5.8625775062 40 -IL13_cell model1_data2 5.8456085207 50 -IL13_cell model1_data2 5.8268553725 60 -IL13_cell model1_data2 5.788996529 80 -IL13_cell model1_data2 5.7514395189 100 -IL13_cell model1_data2 5.7142386514 120 -pIL4Ra model1_data2 0 0 -pIL4Ra model1_data2 1.226370545 4 -pIL4Ra model1_data2 1.4137574553 5 -pIL4Ra model1_data2 1.6865488089 7 -pIL4Ra model1_data2 1.9251305934 10 -pIL4Ra model1_data2 2.0946684877 15 -pIL4Ra model1_data2 2.1495822965 20 -pIL4Ra model1_data2 2.1673789234 30 -pIL4Ra model1_data2 2.160402643 40 -pIL4Ra model1_data2 2.1555141743 45 -pIL4Ra model1_data2 2.140140007 60 -pJAK2 model1_data2 0 0 -pJAK2 model1_data2 0.9775092322 4 -pJAK2 model1_data2 1.2102338931 5 -pJAK2 model1_data2 1.5564372517 7 -pJAK2 model1_data2 1.8547029057 10 -pJAK2 model1_data2 2.0411811523 15 -pJAK2 model1_data2 2.0678225772 20 -pJAK2 model1_data2 2.036161077 25 -pJAK2 model1_data2 1.9807970906 30 -pJAK2 model1_data2 1.8462457668 40 -pJAK2 model1_data2 1.7766686151 45 -pJAK2 model1_data2 1.7084751945 50 -pJAK2 model1_data2 1.57993676 60 -pJAK2 model1_data2 1.3627465451 80 -pJAK2 model1_data2 1.1975863655 100 -pJAK2 model1_data2 1.0750810942 120 -SOCS3mRNA model1_data2 0 0 -SOCS3mRNA model1_data2 54.7642430565 20 -SOCS3mRNA model1_data2 122.3095653067 30 -SOCS3mRNA model1_data2 200.2799185297 40 -SOCS3mRNA model1_data2 364.8542894841 60 -SOCS3mRNA model1_data2 527.0847080334 80 -SOCS3mRNA model1_data2 605.5634673119 90 -SOCS3mRNA model1_data2 682.051466418 100 -SOCS3mRNA model1_data2 829.1181009635 120 -CD274mRNA model1_data2 0.253765268 30 -CD274mRNA model1_data2 0.75699187 60 -CD274mRNA model1_data2 1.2564101197 90 -CD274mRNA model1_data2 1.7202364883 120 -SOCS3 model1_data2 0 0 -SOCS3 model1_data2 14.3117853821 20 -SOCS3 model1_data2 75.5899554372 40 -SOCS3 model1_data2 142.0469159486 60 -SOCS3 model1_data2 192.9222195204 80 -SOCS3 model1_data2 212.2654948088 90 -SOCS3 model1_data2 228.2361275238 100 -SOCS3 model1_data2 252.3148540949 120 -pSTAT5 model1_data2 0 0 -pSTAT5 model1_data2 9.8369693678 5 -pSTAT5 model1_data2 33.0656452021 10 -pSTAT5 model1_data2 54.527209543 15 -pSTAT5 model1_data2 70.5470742104 20 -pSTAT5 model1_data2 81.6623451946 25 -pSTAT5 model1_data2 89.0829820535 30 -pSTAT5 model1_data2 93.8642092334 35 -pSTAT5 model1_data2 96.7932901915 40 -pSTAT5 model1_data2 98.4294175031 45 -pSTAT5 model1_data2 99.1632089984 50 -pSTAT5 model1_data2 98.9293869832 60 -pSTAT5 model1_data2 97.4282678713 70 -pSTAT5 model1_data2 95.3294708838 80 -pSTAT5 model1_data2 92.9819600134 90 -pSTAT5 model1_data2 90.5750752559 100 -pSTAT5 model1_data2 85.9613665968 120 -RecSurf model1_data3 1.3 0 -RecSurf model1_data3 1.3964620975 10 -RecSurf model1_data3 1.3963221638 20 -RecSurf model1_data3 1.3947086697 30 -RecSurf model1_data3 1.3897823176 60 -RecSurf model1_data3 1.3848761993 90 -RecSurf model1_data3 1.3799892107 120 -IL13_cell model1_data3 0.8943932753 5 -IL13_cell model1_data3 1.4008696495 10 -IL13_cell model1_data3 1.7047650106 15 -IL13_cell model1_data3 1.9146632507 20 -IL13_cell model1_data3 2.2033843979 30 -IL13_cell model1_data3 2.397562842 40 -IL13_cell model1_data3 2.5330812121 50 -IL13_cell model1_data3 2.6282078445 60 -IL13_cell model1_data3 2.7417122581 80 -IL13_cell model1_data3 2.7970503617 100 -IL13_cell model1_data3 2.8234478969 120 -pIL4Ra model1_data3 0 0 -pIL4Ra model1_data3 0.2744955332 4 -pIL4Ra model1_data3 0.3216578772 5 -pIL4Ra model1_data3 0.3933567687 7 -pIL4Ra model1_data3 0.4597043427 10 -pIL4Ra model1_data3 0.5098622422 15 -pIL4Ra model1_data3 0.5209355598 17.5 -pIL4Ra model1_data3 0.5274474002 20 -pIL4Ra model1_data3 0.5312455475 22.5 -pIL4Ra model1_data3 0.5334322396 25 -pIL4Ra model1_data3 0.5353274577 30 -pIL4Ra model1_data3 0.5357859807 35 -pIL4Ra model1_data3 0.5357406973 40 -pIL4Ra model1_data3 0.5355190019 45 -pIL4Ra model1_data3 0.5352357244 50 -pIL4Ra model1_data3 0.5346192231 60 -pJAK2 model1_data3 0 0 -pJAK2 model1_data3 0.1344067902 2.5 -pJAK2 model1_data3 0.2642849089 4 -pJAK2 model1_data3 0.3526639874 5 -pJAK2 model1_data3 0.521188721 7 -pJAK2 model1_data3 0.5607467453 7.5 -pJAK2 model1_data3 0.7405410267 10 -pJAK2 model1_data3 0.8896091434 12.5 -pJAK2 model1_data3 1.0088202039 15 -pJAK2 model1_data3 1.1002654097 17.5 -pJAK2 model1_data3 1.1670068 20 -pJAK2 model1_data3 1.241456966 25 -pJAK2 model1_data3 1.2614818608 30 -pJAK2 model1_data3 1.2493256128 35 -pJAK2 model1_data3 1.2189568692 40 -pJAK2 model1_data3 1.1786213845 45 -pJAK2 model1_data3 1.1331778076 50 -pJAK2 model1_data3 1.0374903391 60 -pJAK2 model1_data3 0.8999485342 75 -pJAK2 model1_data3 0.7799605406 90 -pJAK2 model1_data3 0.6795218428 105 -pJAK2 model1_data3 0.5971065427 120 -pSTAT5 model1_data3 0 0 -pSTAT5 model1_data3 2.634483631 5 -pSTAT5 model1_data3 11.7531433701 10 -pSTAT5 model1_data3 24.2748997534 15 -pSTAT5 model1_data3 37.1275881585 20 -pSTAT5 model1_data3 48.5191839147 25 -pSTAT5 model1_data3 57.7724617566 30 -pSTAT5 model1_data3 64.8726802287 35 -pSTAT5 model1_data3 70.0862329678 40 -pSTAT5 model1_data3 73.7501381984 45 -pSTAT5 model1_data3 76.1821120513 50 -pSTAT5 model1_data3 78.3751652344 60 -pSTAT5 model1_data3 78.2340324455 70 -pSTAT5 model1_data3 76.7224653597 80 -pSTAT5 model1_data3 71.6980840069 100 -pSTAT5 model1_data3 65.7771184221 120 -RecSurf model1_data4 1.3 0 -RecSurf model1_data4 1.1049968747 10 -RecSurf model1_data4 1.0898045247 20 -RecSurf model1_data4 1.0774192011 30 -RecSurf model1_data4 1.041131316 60 -RecSurf model1_data4 1.0060657941 90 -RecSurf model1_data4 0.9721785729 120 -IL13_cell model1_data4 8.8441547846 5 -IL13_cell model1_data4 10.572248259 10 -IL13_cell model1_data4 11.0789193277 15 -IL13_cell model1_data4 11.218392462 20 -IL13_cell model1_data4 11.2025120759 30 -IL13_cell model1_data4 11.1076204284 40 -IL13_cell model1_data4 11.0040750003 50 -IL13_cell model1_data4 10.9005095881 60 -IL13_cell model1_data4 10.6965838267 80 -IL13_cell model1_data4 10.497236095 100 -IL13_cell model1_data4 10.3023831625 120 -SOCS3 model1_data4 0 0 -SOCS3 model1_data4 20.248449512 20 -SOCS3 model1_data4 87.1663276835 40 -SOCS3 model1_data4 153.238083014 60 -SOCS3 model1_data4 202.2629088523 80 -SOCS3 model1_data4 235.9720185864 100 -SOCS3 model1_data4 258.9746918774 120 +observableId preequilibrationConditionId simulationConditionId simulation time observableParameters noiseParameters observableTransformation noiseDistribution datasetId +observable_RecSurf model1_data1 1.3 0 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data1_RecSurf +observable_RecSurf model1_data1 1.427926991808253 10 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data1_RecSurf +observable_RecSurf model1_data1 1.4319385888479896 20 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data1_RecSurf +observable_RecSurf model1_data1 1.4320643864867446 30 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data1_RecSurf +observable_RecSurf model1_data1 1.4320684588992234 60 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data1_RecSurf +observable_RecSurf model1_data1 1.4320684590266992 90 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data1_RecSurf +observable_RecSurf model1_data1 1.43206845902619 120 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data1_RecSurf +observable_IL13_cell model1_data1 0.0 0 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data1_IL13_cell +observable_IL13_cell model1_data1 0.0 15 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data1_IL13_cell +observable_IL13_cell model1_data1 0.0 30 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data1_IL13_cell +observable_IL13_cell model1_data1 0.0 60 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data1_IL13_cell +observable_IL13_cell model1_data1 0.0 90 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data1_IL13_cell +observable_IL13_cell model1_data1 0.0 120 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data1_IL13_cell +observable_SOCS3mRNA model1_data1 0.0 0 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data1_SOCS3mRNA +observable_SOCS3mRNA model1_data1 0.0 20 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data1_SOCS3mRNA +observable_SOCS3mRNA model1_data1 0.0 30 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data1_SOCS3mRNA +observable_SOCS3mRNA model1_data1 0.0 40 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data1_SOCS3mRNA +observable_SOCS3mRNA model1_data1 0.0 60 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data1_SOCS3mRNA +observable_SOCS3mRNA model1_data1 0.0 80 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data1_SOCS3mRNA +observable_SOCS3mRNA model1_data1 0.0 90 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data1_SOCS3mRNA +observable_SOCS3mRNA model1_data1 0.0 100 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data1_SOCS3mRNA +observable_SOCS3mRNA model1_data1 0.0 120 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data1_SOCS3mRNA +observable_CD274mRNA model1_data1 0.0 30 scaling_CD274mRNA sd_CD274mRNA_abs;sd_CD274mRNA_rel lin normal model1_data1_CD274mRNA +observable_CD274mRNA model1_data1 0.0 60 scaling_CD274mRNA sd_CD274mRNA_abs;sd_CD274mRNA_rel lin normal model1_data1_CD274mRNA +observable_CD274mRNA model1_data1 0.0 90 scaling_CD274mRNA sd_CD274mRNA_abs;sd_CD274mRNA_rel lin normal model1_data1_CD274mRNA +observable_CD274mRNA model1_data1 0.0 120 scaling_CD274mRNA sd_CD274mRNA_abs;sd_CD274mRNA_rel lin normal model1_data1_CD274mRNA +observable_SOCS3 model1_data1 0.0 0 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data1_SOCS3 +observable_SOCS3 model1_data1 0.0 20 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data1_SOCS3 +observable_SOCS3 model1_data1 0.0 40 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data1_SOCS3 +observable_SOCS3 model1_data1 0.0 60 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data1_SOCS3 +observable_SOCS3 model1_data1 0.0 90 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data1_SOCS3 +observable_SOCS3 model1_data1 0.0 120 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data1_SOCS3 +observable_IL13_cell model1_data2 3.70243485187792 5 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_IL13_cell model1_data2 5.102602250106634 10 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_IL13_cell model1_data2 5.606176816690401 15 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_IL13_cell model1_data2 5.787190744501099 20 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_IL13_cell model1_data2 5.867222662178098 30 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_IL13_cell model1_data2 5.862577971260208 40 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_IL13_cell model1_data2 5.845608616412775 50 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_IL13_cell model1_data2 5.826855380847402 60 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_IL13_cell model1_data2 5.788996524746446 80 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_IL13_cell model1_data2 5.751439517867289 100 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_IL13_cell model1_data2 5.714238651665389 120 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data2_IL13_cell +observable_pIL4Ra model1_data2 0.0 0 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pIL4Ra model1_data2 1.2263705510795004 4 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pIL4Ra model1_data2 1.413757471717009 5 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pIL4Ra model1_data2 1.6865489839348096 7 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pIL4Ra model1_data2 1.9251310384231848 10 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pIL4Ra model1_data2 2.0946686038829796 15 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pIL4Ra model1_data2 2.149583279764929 20 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pIL4Ra model1_data2 2.167379337202789 30 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pIL4Ra model1_data2 2.160402799316903 40 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pIL4Ra model1_data2 2.155514247199555 45 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pIL4Ra model1_data2 2.1401400091498193 60 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data2_pIL4Ra +observable_pJAK2 model1_data2 0.0 0 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 0.9775090135462622 4 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.2102337502061185 5 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.55643711852321 7 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.8547032286290186 10 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 2.04118123737115 15 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 2.067824179111522 20 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 2.0361617107233565 25 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.9807981342609613 30 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.8462463471842605 40 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.7766690672545473 45 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.708475563062574 50 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.5799370097963468 60 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.36274659893853 80 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.1975862105125459 100 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_pJAK2 model1_data2 1.075080923283155 120 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data2_pJAK2 +observable_SOCS3mRNA model1_data2 0.0 0 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data2_SOCS3mRNA +observable_SOCS3mRNA model1_data2 54.76422263520788 20 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data2_SOCS3mRNA +observable_SOCS3mRNA model1_data2 122.30954485929658 30 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data2_SOCS3mRNA +observable_SOCS3mRNA model1_data2 200.27989689561505 40 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data2_SOCS3mRNA +observable_SOCS3mRNA model1_data2 364.8542817421903 60 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data2_SOCS3mRNA +observable_SOCS3mRNA model1_data2 527.0847216592272 80 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data2_SOCS3mRNA +observable_SOCS3mRNA model1_data2 605.5634998378023 90 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data2_SOCS3mRNA +observable_SOCS3mRNA model1_data2 682.0514993897721 100 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data2_SOCS3mRNA +observable_SOCS3mRNA model1_data2 829.1181151263846 120 scaling_SOCS3mRNA sd_SOCS3mRNA_abs;sd_SOCS3mRNA_rel lin normal model1_data2_SOCS3mRNA +observable_CD274mRNA model1_data2 0.2537652255615957 30 scaling_CD274mRNA sd_CD274mRNA_abs;sd_CD274mRNA_rel lin normal model1_data2_CD274mRNA +observable_CD274mRNA model1_data2 0.7569918538241007 60 scaling_CD274mRNA sd_CD274mRNA_abs;sd_CD274mRNA_rel lin normal model1_data2_CD274mRNA +observable_CD274mRNA model1_data2 1.2564101869971849 90 scaling_CD274mRNA sd_CD274mRNA_abs;sd_CD274mRNA_rel lin normal model1_data2_CD274mRNA +observable_CD274mRNA model1_data2 1.72023651747127 120 scaling_CD274mRNA sd_CD274mRNA_abs;sd_CD274mRNA_rel lin normal model1_data2_CD274mRNA +observable_SOCS3 model1_data2 0.0 0 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data2_SOCS3 +observable_SOCS3 model1_data2 14.311803811021125 20 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data2_SOCS3 +observable_SOCS3 model1_data2 75.58996639105979 40 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data2_SOCS3 +observable_SOCS3 model1_data2 142.04692087035627 60 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data2_SOCS3 +observable_SOCS3 model1_data2 192.92224880264308 80 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data2_SOCS3 +observable_SOCS3 model1_data2 212.26555835074686 90 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data2_SOCS3 +observable_SOCS3 model1_data2 228.23619464817077 100 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data2_SOCS3 +observable_SOCS3 model1_data2 252.31489068281527 120 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data2_SOCS3 +observable_pSTAT5 model1_data2 0.0 0 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 9.83696908739854 5 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 33.06562649345129 10 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 54.52720009429548 15 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 70.54705569765059 20 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 81.66234681955419 25 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 89.08297754812236 30 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 93.86421025509601 35 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 96.79329591396666 40 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 98.42942572285993 45 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 99.16321780420404 50 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 98.92939346617315 60 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 97.42826911241804 70 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 95.32946389492542 80 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 92.98193745496619 90 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 90.57504971247687 100 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_pSTAT5 model1_data2 85.96134731553998 120 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data2_pSTAT5 +observable_RecSurf model1_data3 1.3 0 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data3_RecSurf +observable_RecSurf model1_data3 1.396462100259517 10 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data3_RecSurf +observable_RecSurf model1_data3 1.396322171155627 20 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data3_RecSurf +observable_RecSurf model1_data3 1.3947086687227774 30 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data3_RecSurf +observable_RecSurf model1_data3 1.389782317398023 60 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data3_RecSurf +observable_RecSurf model1_data3 1.3848761990152654 90 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data3_RecSurf +observable_RecSurf model1_data3 1.3799892103568623 120 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data3_RecSurf +observable_IL13_cell model1_data3 0.8943932743073547 5 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_IL13_cell model1_data3 1.40086966736291 10 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_IL13_cell model1_data3 1.704765175993059 15 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_IL13_cell model1_data3 1.9146636085914057 20 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_IL13_cell model1_data3 2.203384354718069 30 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_IL13_cell model1_data3 2.3975627838542746 40 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_IL13_cell model1_data3 2.533081450413838 50 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_IL13_cell model1_data3 2.628207921587208 60 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_IL13_cell model1_data3 2.741713850921113 80 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_IL13_cell model1_data3 2.797053241049496 100 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_IL13_cell model1_data3 2.8234503684628374 120 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data3_IL13_cell +observable_pIL4Ra model1_data3 0.0 0 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.2744955361452215 4 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.3216578765651934 5 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.39335677856917906 7 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.45970435218137606 10 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.5098623300755035 15 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.5209357403426477 17.5 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.5274475864948982 20 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.5312457044727048 22.5 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.5334323595580074 25 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.5353274272019388 30 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.5357859091515362 35 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.5357406675068845 40 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.5355190602169534 45 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.535235844323706 50 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pIL4Ra model1_data3 0.5346192505068957 60 scaling_pIL4Ra sd_pIL4Ra_abs;sd_pIL4Ra_rel lin normal model1_data3_pIL4Ra +observable_pJAK2 model1_data3 0.0 0 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.1344067630426549 2.5 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.26428491203471793 4 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.35266396027701236 5 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.5211886803349022 7 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.5607467123341389 7.5 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.7405410107564824 10 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.8896091296515969 12.5 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 1.008820151880419 15 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 1.1002652576108447 17.5 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 1.1670065800687508 20 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 1.2414567532114642 25 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 1.2614816212828381 30 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 1.2493251950844853 35 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 1.2189564659375824 40 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 1.1786215213433715 45 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 1.1331782950408023 50 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 1.0374905773437633 60 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.8999493222200821 75 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.7799631825623071 90 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.6795245688432037 105 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pJAK2 model1_data3 0.5971088131766777 120 scaling_pJAK2 sd_pJAK2_abs;sd_pJAK2_rel lin normal model1_data3_pJAK2 +observable_pSTAT5 model1_data3 0.0 0 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 2.6344837045638383 5 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 11.753143123803058 10 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 24.274895761901043 15 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 37.1275678656001 20 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 48.51915536341248 25 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 57.77245090523443 30 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 64.87268609561487 35 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 70.08623714198566 40 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 73.75011775813557 45 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 76.18206463993167 50 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 78.37513831460014 60 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 78.23401987966972 70 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 76.72268279582025 80 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 71.69845545903121 100 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_pSTAT5 model1_data3 65.77744144397786 120 sd_pSTAT5_abs;sd_pSTAT5_rel lin normal model1_data3_pSTAT5 +observable_RecSurf model1_data4 1.3 0 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data4_RecSurf +observable_RecSurf model1_data4 1.104996842564384 10 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data4_RecSurf +observable_RecSurf model1_data4 1.089804529700224 20 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data4_RecSurf +observable_RecSurf model1_data4 1.0774191998292082 30 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data4_RecSurf +observable_RecSurf model1_data4 1.0411313159369644 60 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data4_RecSurf +observable_RecSurf model1_data4 1.006065794219118 90 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data4_RecSurf +observable_RecSurf model1_data4 0.9721785730455508 120 sd_RecSurf_abs;sd_RecSurf_rel lin normal model1_data4_RecSurf +observable_IL13_cell model1_data4 8.844157651297358 5 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_IL13_cell model1_data4 10.572248762935684 10 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_IL13_cell model1_data4 11.078919386833286 15 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_IL13_cell model1_data4 11.218392512716926 20 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_IL13_cell model1_data4 11.202512843128465 30 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_IL13_cell model1_data4 11.10762120330836 40 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_IL13_cell model1_data4 11.004075298162057 50 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_IL13_cell model1_data4 10.900509979762443 60 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_IL13_cell model1_data4 10.696583841810686 80 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_IL13_cell model1_data4 10.497236097510338 100 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_IL13_cell model1_data4 10.302383164662874 120 scaling_IL13_cell sd_IL13_cell_abs;sd_IL13_cell_rel lin normal model1_data4_IL13_cell +observable_SOCS3 model1_data4 0.0 0 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data4_SOCS3 +observable_SOCS3 model1_data4 20.24844603287298 20 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data4_SOCS3 +observable_SOCS3 model1_data4 87.16633006547572 40 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data4_SOCS3 +observable_SOCS3 model1_data4 153.23804436505284 60 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data4_SOCS3 +observable_SOCS3 model1_data4 202.26285995257064 80 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data4_SOCS3 +observable_SOCS3 model1_data4 235.97201041152738 100 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data4_SOCS3 +observable_SOCS3 model1_data4 258.9747149860635 120 sd_SOCS3_abs;sd_SOCS3_rel lin normal model1_data4_SOCS3 From f3b7b88ef891bc30275392ac95d33f06bab5bb21 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 15:53:10 +0000 Subject: [PATCH 13/18] Use AMICI >= 1.2.0 in the Simulate workflow Released AMICI instead of a development version. The development version, like AMICI 0.34.2 and 1.0.0, ignores the condition table value of a parameter with an initial assignment where the parameter is used in another initial assignment: the virus dose u_2 of Liu_IFACPapersOnLine2025 (initial assignment u_2 = 1e9, condition ctrl u_2 = 0) then also enters the initial virus V = u_2 of the control condition. With AMICI 1.2.0, Liu_IFACPapersOnLine2025 reproduces its simulation table, with the log-likelihood of the paper (-723.357), and is removed from KNOWN_FAILURES, which is now empty. simulate.py imports simulate_petab, rdatas_to_simulation_df, LLH and RDATAS from their location in AMICI 1.2.0. All problems reproduce their simulation tables and pass the tolerance check with AMICI 1.2.0. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/simulate.yml | 12 ++++++------ src/python/simulate.py | 6 +++--- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index 61c94d45..95436d5c 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -30,10 +30,11 @@ jobs: AMICI_PARALLEL_COMPILE: "" run: | pip install --upgrade pip - # TODO: once amici 1.0 is released, replace with: - # pip install amici[petab] - pip install petab - pip install "git+https://github.com/dweindl/amici.git@6a85422ad233fc268fb71272bba39f5b82db387a#egg=amici&subdirectory=python/sdist" + # >= 1.2.0: some earlier versions (e.g. 0.34.2 and 1.0.0) ignore the + # condition table value of a parameter with an initial assignment + # where it is used in another initial assignment + # (Liu_IFACPapersOnLine2025) + pip install "amici[petab]>=1.2.0" - name: Simulate and compare to the simulation tables env: @@ -44,8 +45,7 @@ jobs: # simulations at the nominal parameters. They are simulated and # reported, but do not fail the job. Remove a problem from this list # once its simulation table has been fixed. - KNOWN_FAILURES: >- - Liu_IFACPapersOnLine2025 + KNOWN_FAILURES: "" run: | failed=() known_failed=() diff --git a/src/python/simulate.py b/src/python/simulate.py index 5162056b..08530d4d 100755 --- a/src/python/simulate.py +++ b/src/python/simulate.py @@ -15,8 +15,8 @@ import argparse import benchmark_models_petab import amici.sim.sundials as ass -from amici.importers.petab.v1 import ( - import_petab_problem, +from amici.importers.petab.v1 import import_petab_problem +from amici.sim.sundials.petab.v1 import ( rdatas_to_simulation_df, simulate_petab, LLH, @@ -364,7 +364,7 @@ def simulate( solver.set_absolute_tolerance(settings["atol"]) solver.set_max_steps(int(settings["max_steps"])) res = simulate_petab( - problem, model, solver=solver, num_threads=num_threads + problem, amici_model=model, solver=solver, num_threads=num_threads ) if verbose: From 521316cffbc748e1581e477be2cdb6cba5bbf694 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 16:24:15 +0000 Subject: [PATCH 14/18] Require a simulation table for every problem simulate.py --check-simulations fails for a problem without a simulation table instead of skipping it, so that new problems need one; all problems have one. CONTRIBUTING.md describes the simulation table, how to generate it (simulate.py --output) and how it is checked. --output always simulates all conditions, also for problems with a subset of the conditions in solver_settings.yaml. Problems without a PEtab v1 encoding are skipped (simulate.py does not support PEtab v2 yet), unknown problem IDs are an error. Co-Authored-By: Claude Opus 5.5 --- CONTRIBUTING.md | 19 +++++++++++++++++-- src/python/simulate.py | 39 ++++++++++++++++++++++++--------------- 2 files changed, 41 insertions(+), 17 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 594c32f1..60d338f9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -4,8 +4,9 @@ Simply create a new branch and open a pull request with your files. We will then Each problem lives under `problems//`, with at least one of `v1/`/`v2/` present: - `v1/` holds the problem in PEtab v1 format: `problem.yaml`, `model.xml`, - `conditions.tsv`, `measurements.tsv`, `observables.tsv`, `parameters.tsv`, and optionally - `simulations.tsv` and `visualizations.tsv`. + `conditions.tsv`, `measurements.tsv`, `observables.tsv`, `parameters.tsv`, + `simulations.tsv` (see [Simulation table](#simulation-table)), and optionally + `visualizations.tsv`. - `v2/` holds the same problem in PEtab v2 format. - `metadata.yaml` contains version-independent metadata. Currently, this is a single-sentence `short_description` of the problem, stating the type of model (e.g., biochemical reaction network, @@ -22,6 +23,20 @@ Each problem lives under `problems//`, with at least one of `v1/`/`v2 PEtab files themselves (e.g. raw data, scripts, notebooks, figures). There's no prescribed structure — organize it however fits your problem. +## Simulation table + +`v1/simulations.tsv` holds the simulations of the model at the nominal parameters of the +parameter table, one row per row of the measurement table: the measurement table with the +column `measurement` replaced by `simulation`. It can be generated with AMICI: +```bash +python src/python/simulate.py --output problems//v1/simulations.tsv +``` +The [Simulate](.github/workflows/simulate.yml) workflow checks that every problem reproduces +its simulation table (`simulate.py --check-simulations`), with the solver settings +of [`solver_settings.yaml`](src/python/solver_settings.yaml). Check that these settings are +accurate enough for a new problem with `simulate.py --check-simulation-tolerances`, +and add problem-specific settings to `solver_settings.yaml` if they are not. + ## v1/v2 equivalence A problem's `v1/` and `v2/` encodings must represent *exactly* the same mathematical problem: diff --git a/src/python/simulate.py b/src/python/simulate.py index 08530d4d..06911eb9 100755 --- a/src/python/simulate.py +++ b/src/python/simulate.py @@ -88,8 +88,8 @@ def parse_cli_args(): action="store_true", help=( "Compare the simulations against the simulation table of the " - "problem and exit with status 1 if they differ. Problems " - "without a simulation table are skipped." + "problem and exit with status 1 if they differ or if the " + "problem has no simulation table." ), ) parser.add_argument( @@ -107,7 +107,7 @@ def parse_cli_args(): help=( "Simulate all conditions, also for problems with `conditions` " "in the solver settings, for which by default only a subset of " - "the conditions is simulated." + "the conditions is simulated (always all with --output)." ), ) parser.add_argument( @@ -491,16 +491,24 @@ def main(): print(args.problem_id) print("-" * len(args.problem_id), flush=True) + if args.problem_id not in benchmark_models_petab.MODELS: + sys.exit(f"Unknown problem: {args.problem_id}") + if args.problem_id not in benchmark_models_petab.v1.MODELS: + # TODO: simulate PEtab v2 problems + print("The problem has no PEtab v1 encoding, skipping it.") + print() + return + problem = benchmark_models_petab.get_problem(args.problem_id) expected_df = benchmark_models_petab.get_simulation_df(args.problem_id) - if ( - args.check_simulations - and expected_df is None - and not args.check_simulation_tolerances - ): - print("No simulation table, skipping the simulation.") + if args.check_simulations and expected_df is None: + print( + "The problem has no simulation table. Add one, e.g. with " + "--output, see CONTRIBUTING.md." + ) + print(f"FAILED: {args.problem_id}") print() - return + sys.exit(1) settings = get_solver_settings(args.problem_id) print(f"Solver settings: {settings}") @@ -517,7 +525,11 @@ def main(): ) n_conditions = settings.pop("conditions", None) - if n_conditions is not None and not args.all_conditions: + if ( + n_conditions is not None + and not args.all_conditions + and not args.output + ): problem, measurement_df, conditions = select_conditions( problem, measurement_df, int(n_conditions) ) @@ -569,10 +581,7 @@ def main(): num_threads=args.num_threads, ) - if args.check_simulations and expected_df is None: - print("No simulation table, skipping the comparison.") - print() - elif args.check_simulations: + if args.check_simulations: expected_df = unflatten_observable_ids( expected_df, measurement_df[petab_C.OBSERVABLE_ID].unique() ) From 277dffcef1dcc5a613650bd530e089165f82765e Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 16:35:48 +0000 Subject: [PATCH 15/18] Fix issues found in review - Select the rows of a subset of conditions with one helper, rows_in_conditions, for both the simulated problem and the simulation table; it normalizes missing condition IDs to "" as petab.v1.get_simulation_conditions does, so rows without a preequilibration condition are no longer dropped when other rows have one. - Filter the simulation table to the subset only with --check-simulations. - Check that the simulation table has the columns time and simulation. - Treat an empty problem entry in solver_settings.yaml as no overrides. - Run the workflow also for pull requests that change solver_settings.yaml or the benchmark_models_petab package. - Accept any whitespace, also newlines, between the IDs of KNOWN_FAILURES. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/simulate.yml | 5 +++- src/python/simulate.py | 46 +++++++++++++++++++++------------- 2 files changed, 33 insertions(+), 18 deletions(-) diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index 95436d5c..d90d4399 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -7,6 +7,8 @@ on: paths: - 'problems/**' - 'src/python/simulate.py' + - 'src/python/solver_settings.yaml' + - 'src/python/benchmark_models_petab/**' - '.github/workflows/simulate.yml' workflow_dispatch: merge_group: @@ -52,7 +54,8 @@ jobs: fixed=() for problem in `ls -1 problems/`; do known=false - [[ " $KNOWN_FAILURES " == *" $problem "* ]] && known=true + # unquoted: any whitespace (also newlines) separates the IDs + [[ " "$(echo $KNOWN_FAILURES)" " == *" $problem "* ]] && known=true if python src/python/simulate.py "$problem" -j \ --check-simulations; then $known && fixed+=("$problem") diff --git a/src/python/simulate.py b/src/python/simulate.py index 06911eb9..683c7b4e 100755 --- a/src/python/simulate.py +++ b/src/python/simulate.py @@ -203,7 +203,12 @@ def compare_simulations( ------- Whether the simulations agree. """ - for column in [petab_C.OBSERVABLE_ID, petab_C.SIMULATION_CONDITION_ID]: + for column in [ + petab_C.OBSERVABLE_ID, + petab_C.SIMULATION_CONDITION_ID, + petab_C.TIME, + petab_C.SIMULATION, + ]: if column not in expected_df: print(f"The simulation table has no column `{column}`.") return False @@ -269,6 +274,23 @@ def compare_simulations( return ok +def rows_in_conditions(df: pd.DataFrame, conditions: pd.DataFrame): + """Get a boolean mask of the rows of a measurement/simulation table that + belong to one of the given simulation conditions. + + Missing condition IDs are normalized to ``""``, as in + :func:`petab.v1.get_simulation_conditions`. + """ + columns = list(conditions.columns) + keys = conditions.astype(object).fillna("").astype(str).drop_duplicates() + return ( + _row_keys(df)[columns] + .merge(keys, how="left", indicator=True)["_merge"] + .eq("both") + .to_numpy() + ) + + def select_conditions(problem, measurement_df: pd.DataFrame, n: int): """Restrict a problem to ``n`` of its simulation conditions. @@ -296,14 +318,9 @@ def select_conditions(problem, measurement_df: pd.DataFrame, n: int): selected = conditions.iloc[ np.unique(np.linspace(0, len(conditions) - 1, n).round().astype(int)) ] - columns = list(selected.columns) - in_selected = ( - problem.measurement_df[columns] - .merge(selected, how="left", indicator=True)["_merge"] - .eq("both") - .to_numpy() - ) - index = problem.measurement_df.index[in_selected] + index = problem.measurement_df.index[ + rows_in_conditions(problem.measurement_df, selected) + ] problem = copy.copy(problem) problem.measurement_df = problem.measurement_df.loc[index] @@ -320,7 +337,7 @@ def get_solver_settings(problem_id: str) -> dict: settings = yaml.safe_load(f) return { **settings["default"], - **(settings.get("problems") or {}).get(problem_id, {}), + **((settings.get("problems") or {}).get(problem_id) or {}), } @@ -539,15 +556,10 @@ def main(): "--all-conditions to simulate all)." ) print() - if expected_df is not None: + if args.check_simulations: # compare only the rows of the selected conditions - columns = list(conditions.columns) - keys = _row_keys(measurement_df)[columns].drop_duplicates() expected_df = expected_df.loc[ - _row_keys(expected_df)[columns] - .merge(keys, how="left", indicator=True)["_merge"] - .eq("both") - .to_numpy() + rows_in_conditions(expected_df, conditions) ] res, simulation_df = simulate( From f43a0de99b4c448040420f66fd43842c50d2312d Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 16:39:38 +0000 Subject: [PATCH 16/18] Remove unused code - KNOWN_FAILURES in the Simulate workflow: it is empty, as all problems reproduce their simulation tables; a mismatch now always fails the job. - The rtol and atol parameters of compare_simulations, which were always the hardcoded COMPARISON_RTOL and COMPARISON_ATOL. Co-Authored-By: Claude Opus 5.5 --- .github/workflows/simulate.yml | 30 +++++------------------------- src/python/simulate.py | 17 +++++++++-------- 2 files changed, 14 insertions(+), 33 deletions(-) diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index d90d4399..4d1f67ce 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -41,29 +41,13 @@ jobs: - name: Simulate and compare to the simulation tables env: AMICI_PARALLEL_COMPILE: "" - # The problems are simulated with the solver settings of - # src/python/solver_settings.yaml. - # Problems whose simulation tables are known not to match the - # simulations at the nominal parameters. They are simulated and - # reported, but do not fail the job. Remove a problem from this list - # once its simulation table has been fixed. - KNOWN_FAILURES: "" + # The problems are simulated with the solver settings of + # src/python/solver_settings.yaml. run: | failed=() - known_failed=() - fixed=() for problem in `ls -1 problems/`; do - known=false - # unquoted: any whitespace (also newlines) separates the IDs - [[ " "$(echo $KNOWN_FAILURES)" " == *" $problem "* ]] && known=true - if python src/python/simulate.py "$problem" -j \ - --check-simulations; then - $known && fixed+=("$problem") - elif $known; then - known_failed+=("$problem") - else - failed+=("$problem") - fi + python src/python/simulate.py "$problem" -j --check-simulations \ + || failed+=("$problem") echo echo done @@ -72,10 +56,6 @@ jobs: echo "### Comparison to the simulation tables" echo echo "Failed: ${failed[*]:-none}" - echo - echo "Known failures (\`KNOWN_FAILURES\`): ${known_failed[*]:-none}" - echo - echo "Known failures that now pass, to be removed from \`KNOWN_FAILURES\`: ${fixed[*]:-none}" } | tee -a "$GITHUB_STEP_SUMMARY" - [ ${#failed[@]} -eq 0 ] && [ ${#fixed[@]} -eq 0 ] + [ ${#failed[@]} -eq 0 ] diff --git a/src/python/simulate.py b/src/python/simulate.py index 683c7b4e..47dd0711 100755 --- a/src/python/simulate.py +++ b/src/python/simulate.py @@ -184,10 +184,9 @@ def unflatten(id_): def compare_simulations( simulation_df: pd.DataFrame, expected_df: pd.DataFrame, - rtol: float = COMPARISON_RTOL, - atol: float = COMPARISON_ATOL, ) -> bool: - """Compare simulations against a reference simulation table. + """Compare simulations against a reference simulation table, with the + tolerances :data:`COMPARISON_RTOL` and :data:`COMPARISON_ATOL`. Parameters ---------- @@ -196,8 +195,6 @@ def compare_simulations( of :data:`KEY_COLUMNS`. expected_df: The reference simulation table. - rtol, atol: - Relative and absolute tolerance, see :func:`numpy.isclose`. Returns ------- @@ -242,7 +239,11 @@ def compare_simulations( both = merged[merged["_merge"] == "both"] close = np.isclose( - both["actual"], both["expected"], rtol=rtol, atol=atol, equal_nan=True + both["actual"], + both["expected"], + rtol=COMPARISON_RTOL, + atol=COMPARISON_ATOL, + equal_nan=True, ) if not close.all(): ok = False @@ -255,7 +256,7 @@ def compare_simulations( ].abs().replace(0, np.nan) print( f"{len(mismatches)} of {len(both)} simulations differ " - f"(rtol={rtol}, atol={atol}); largest differences:" + f"(rtol={COMPARISON_RTOL}, atol={COMPARISON_ATOL}); largest differences:" ) print( mismatches.sort_values("abs_diff", ascending=False)[ @@ -269,7 +270,7 @@ def compare_simulations( if ok: print( f"All {len(both)} simulations agree with the simulation table " - f"(rtol={rtol}, atol={atol})." + f"(rtol={COMPARISON_RTOL}, atol={COMPARISON_ATOL})." ) return ok From 2b719212e9535df1bc862b6b8f54a496b5689877 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 17:45:31 +0100 Subject: [PATCH 17/18] remove comment --- .github/workflows/simulate.yml | 4 ---- 1 file changed, 4 deletions(-) diff --git a/.github/workflows/simulate.yml b/.github/workflows/simulate.yml index 4d1f67ce..40e6ce6b 100644 --- a/.github/workflows/simulate.yml +++ b/.github/workflows/simulate.yml @@ -32,10 +32,6 @@ jobs: AMICI_PARALLEL_COMPILE: "" run: | pip install --upgrade pip - # >= 1.2.0: some earlier versions (e.g. 0.34.2 and 1.0.0) ignore the - # condition table value of a parameter with an initial assignment - # where it is used in another initial assignment - # (Liu_IFACPapersOnLine2025) pip install "amici[petab]>=1.2.0" - name: Simulate and compare to the simulation tables From caae923b5c01233c3a41702ba294a3af4e7fa2a5 Mon Sep 17 00:00:00 2001 From: Dilan Pathirana Date: Mon, 2 Nov 2026 18:10:38 +0000 Subject: [PATCH 18/18] Check 200 instead of 1000 conditions of Froehlich_CellSystems2018 The 200 evenly spaced conditions cover all 143 cell lines and all drugs, like 1000; they take about 22 instead of 120 seconds on 4 cores. Co-Authored-By: Claude Opus 5.5 --- src/python/solver_settings.yaml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/python/solver_settings.yaml b/src/python/solver_settings.yaml index d62b4b55..73ed2f79 100644 --- a/src/python/solver_settings.yaml +++ b/src/python/solver_settings.yaml @@ -37,8 +37,9 @@ problems: # default: change 2.5e-4 rtol: 1.0e-10 atol: 1.0e-18 - # all 9169 conditions take about 20 minutes on 4 cores, 1000 about 2 - conditions: 1000 + # all 9169 conditions take about 20 minutes on 4 cores; 200 take about + # 25 seconds and cover all 143 cell lines and all drugs + conditions: 200 Isensee_JCB2018: # default: change 1.9e-5 rtol: 1.0e-10