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Add published optimum to Blasi PEtab Select problem (#163)
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‎Benchmark-Models/Blasi_CellSystems2016/petab_select/03_create_petab.py‎

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@@ -43,7 +43,7 @@
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measurements0_grouped = {k: v for k, v in petab_problem0.measurement_df.groupby(OBSERVABLE_ID)}
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condition_id = 'condition'
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noise = 'sigma'
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noise = 'sigma_'
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def convert_observable_id(id0: str) -> str:
@@ -90,17 +90,37 @@ def convert_observable_id(id0: str) -> str:
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measurement_dicts.append(measurement_dict)
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# Create parameters
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## Optimal parameters, reported in paper, taken from MATLAB implementation
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published_optimal_parameters = {
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'a_b': 0.0667579590703576,
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'a_0ac_k08': 0.0272712553008379,
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'a_k05_k05k12': 2.06203333647699,
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'a_k12_k05k12': 0.551940805482671,
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'a_k16_k12k16': 0.695938574109670,
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'a_k05k12_k05k08k12': 0.325297000106462,
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'a_k12k16_k08k12k16': 2.20553079904637,
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'a_k08k12k16_4ac': 3.59169549625268,
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}
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parameter_dicts = []
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## Motif-specific
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for parameter_dict0 in motif_specific_parameters.values():
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parameter_dict = {
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PARAMETER_ID: parameter_dict0['id'],
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PARAMETER_NAME: parameter_dict0['name'],
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PARAMETER_SCALE: LOG10,
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LOWER_BOUND: '1e-12',
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LOWER_BOUND: '1e-3',
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UPPER_BOUND: '1e3',
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NOMINAL_VALUE: 1,
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ESTIMATE: 0,
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NOMINAL_VALUE: (
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published_optimal_parameters[parameter_dict0['id']] / published_optimal_parameters['a_b']
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if parameter_dict0['id'] in published_optimal_parameters
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else 1
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),
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ESTIMATE: (
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1
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if parameter_dict0['id'] in published_optimal_parameters
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else 0
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),
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}
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parameter_dicts.append(parameter_dict)
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# ## Switch
@@ -120,9 +140,9 @@ def convert_observable_id(id0: str) -> str:
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PARAMETER_ID: parameter_dict0['id'],
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PARAMETER_NAME: parameter_dict0['name'],
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PARAMETER_SCALE: LOG10,
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LOWER_BOUND: '1e-12',
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LOWER_BOUND: '1e-3',
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UPPER_BOUND: '1e3',
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NOMINAL_VALUE: 0,
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NOMINAL_VALUE: published_optimal_parameters['a_b'],
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ESTIMATE: 1,
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}
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elif parameter_dict0['id'] == 'da_b':
@@ -142,10 +162,10 @@ def convert_observable_id(id0: str) -> str:
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PARAMETER_ID: noise,
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PARAMETER_NAME: noise,
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PARAMETER_SCALE: LOG10,
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LOWER_BOUND: '1e-12',
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LOWER_BOUND: '1e-3',
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UPPER_BOUND: '1e3',
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NOMINAL_VALUE: 0.1,
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ESTIMATE: 1,
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NOMINAL_VALUE: 1,
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ESTIMATE: 0,
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}
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)
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‎Benchmark-Models/Blasi_CellSystems2016/petab_select/README.md‎

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@@ -4,6 +4,8 @@ This is a fresh implementation of the model from `Blasi_CellSystems2016`, to fac
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Specifically, this model contains parameters to enable model selection with motif-specific acetylation rates. These additional parameters are implemented as multipliers of the basal acetylation rate `a_b`.
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Note that this means any motif-specific parameter `a_*` (except the basal acetylation rate `a_b`) can be fixed to 1 to "turn off" the motif-specific acetylation rate, or estimated to "turn on" the motif-specific acetylation rate.
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The nominal values in the parameter table (`output/petab/parameters.tsv`) are taken from the original MATLAB implementation and match the values reported in the supplementary of the original paper. However, they have been divided by the nominal value for `a_b`, because the motif-specific parameters are multipliers in this implementation.
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The only thing taken from the main PEtab problem are the measurements, the remaining PEtab (Select) files and the model were generated with the provided scripts.
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# Python requirements

‎Benchmark-Models/Blasi_CellSystems2016/petab_select/output/petab/observables.tsv‎

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@@ -1,16 +1,16 @@
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observableId observableName observableFormula observableTransformation noiseFormula noiseDistribution
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observable_x_0ac y_{0ac} x_0ac log sigma normal
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observable_x_4ac y_{4ac} x_4ac log sigma normal
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observable_x_k12 y_{k12} x_k12 log sigma normal
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observable_x_k12k16 y_{k12k16} x_k12k16 log sigma normal
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observable_x_k16 y_{k16} x_k16 log sigma normal
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observable_x_k05 y_{k05} x_k05 log sigma normal
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observable_x_k05k12 y_{k05k12} x_k05k12 log sigma normal
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observable_x_k05k12k16 y_{k05k12k16} x_k05k12k16 log sigma normal
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observable_x_k05k08 y_{k05k08} x_k05k08 log sigma normal
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observable_x_k05k08k12 y_{k05k08k12} x_k05k08k12 log sigma normal
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observable_x_k05k08k16 y_{k05k08k16} x_k05k08k16 log sigma normal
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observable_x_k08 y_{k08} x_k08 log sigma normal
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observable_x_k08k12 y_{k08k12} x_k08k12 log sigma normal
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observable_x_k08k12k16 y_{k08k12k16} x_k08k12k16 log sigma normal
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observable_x_k08k16 y_{k08k16} x_k08k16 log sigma normal
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observable_x_0ac y_{0ac} x_0ac log sigma_ normal
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observable_x_4ac y_{4ac} x_4ac log sigma_ normal
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observable_x_k12 y_{k12} x_k12 log sigma_ normal
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observable_x_k12k16 y_{k12k16} x_k12k16 log sigma_ normal
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observable_x_k16 y_{k16} x_k16 log sigma_ normal
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observable_x_k05 y_{k05} x_k05 log sigma_ normal
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observable_x_k05k12 y_{k05k12} x_k05k12 log sigma_ normal
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observable_x_k05k12k16 y_{k05k12k16} x_k05k12k16 log sigma_ normal
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observable_x_k05k08 y_{k05k08} x_k05k08 log sigma_ normal
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observable_x_k05k08k12 y_{k05k08k12} x_k05k08k12 log sigma_ normal
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observable_x_k05k08k16 y_{k05k08k16} x_k05k08k16 log sigma_ normal
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observable_x_k08 y_{k08} x_k08 log sigma_ normal
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observable_x_k08k12 y_{k08k12} x_k08k12 log sigma_ normal
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observable_x_k08k12k16 y_{k08k12k16} x_k08k12k16 log sigma_ normal
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observable_x_k08k16 y_{k08k16} x_k08k16 log sigma_ normal

‎Benchmark-Models/Blasi_CellSystems2016/petab_select/output/petab/parameters.tsv‎

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Original file line numberDiff line numberDiff line change
@@ -1,36 +1,36 @@
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parameterId parameterName parameterScale lowerBound upperBound nominalValue estimate
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a_0ac_k05 a_{0ac\\rightarrow k05} log10 1e-12 1e3 1.0 0
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a_0ac_k08 a_{0ac\\rightarrow k08} log10 1e-12 1e3 1.0 0
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a_0ac_k12 a_{0ac\\rightarrow k12} log10 1e-12 1e3 1.0 0
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a_0ac_k16 a_{0ac\\rightarrow k16} log10 1e-12 1e3 1.0 0
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a_k05_k05k08 a_{k05\\rightarrow k05k08} log10 1e-12 1e3 1.0 0
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a_k05_k05k12 a_{k05\\rightarrow k05k12} log10 1e-12 1e3 1.0 0
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a_k05_k05k16 a_{k05\\rightarrow k05k16} log10 1e-12 1e3 1.0 0
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a_k08_k05k08 a_{k08\\rightarrow k05k08} log10 1e-12 1e3 1.0 0
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a_k08_k08k12 a_{k08\\rightarrow k08k12} log10 1e-12 1e3 1.0 0
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a_k08_k08k16 a_{k08\\rightarrow k08k16} log10 1e-12 1e3 1.0 0
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a_k12_k05k12 a_{k12\\rightarrow k05k12} log10 1e-12 1e3 1.0 0
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a_k12_k08k12 a_{k12\\rightarrow k08k12} log10 1e-12 1e3 1.0 0
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a_k12_k12k16 a_{k12\\rightarrow k12k16} log10 1e-12 1e3 1.0 0
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a_k16_k05k16 a_{k16\\rightarrow k05k16} log10 1e-12 1e3 1.0 0
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a_k16_k08k16 a_{k16\\rightarrow k08k16} log10 1e-12 1e3 1.0 0
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a_k16_k12k16 a_{k16\\rightarrow k12k16} log10 1e-12 1e3 1.0 0
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a_k05k08_k05k08k12 a_{k05k08\\rightarrow k05k08k12} log10 1e-12 1e3 1.0 0
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a_k05k08_k05k08k16 a_{k05k08\\rightarrow k05k08k16} log10 1e-12 1e3 1.0 0
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a_k05k12_k05k08k12 a_{k05k12\\rightarrow k05k08k12} log10 1e-12 1e3 1.0 0
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a_k05k12_k05k12k16 a_{k05k12\\rightarrow k05k12k16} log10 1e-12 1e3 1.0 0
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a_k05k16_k05k08k16 a_{k05k16\\rightarrow k05k08k16} log10 1e-12 1e3 1.0 0
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a_k05k16_k05k12k16 a_{k05k16\\rightarrow k05k12k16} log10 1e-12 1e3 1.0 0
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a_k08k12_k05k08k12 a_{k08k12\\rightarrow k05k08k12} log10 1e-12 1e3 1.0 0
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a_k08k12_k08k12k16 a_{k08k12\\rightarrow k08k12k16} log10 1e-12 1e3 1.0 0
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a_k08k16_k05k08k16 a_{k08k16\\rightarrow k05k08k16} log10 1e-12 1e3 1.0 0
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a_k08k16_k08k12k16 a_{k08k16\\rightarrow k08k12k16} log10 1e-12 1e3 1.0 0
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a_k12k16_k05k12k16 a_{k12k16\\rightarrow k05k12k16} log10 1e-12 1e3 1.0 0
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a_k12k16_k08k12k16 a_{k12k16\\rightarrow k08k12k16} log10 1e-12 1e3 1.0 0
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a_k05k08k12_4ac a_{k05k08k12\\rightarrow 4ac} log10 1e-12 1e3 1.0 0
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a_k05k08k16_4ac a_{k05k08k16\\rightarrow 4ac} log10 1e-12 1e3 1.0 0
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a_k05k12k16_4ac a_{k05k12k16\\rightarrow 4ac} log10 1e-12 1e3 1.0 0
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a_k08k12k16_4ac a_{k08k12k16\\rightarrow 4ac} log10 1e-12 1e3 1.0 0
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a_b a_b log10 1e-12 1e3 0.0 1
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a_0ac_k05 a_{0ac\\rightarrow k05} log10 1e-3 1e3 1.0 0
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a_0ac_k08 a_{0ac\\rightarrow k08} log10 1e-3 1e3 0.4085094224060409 1
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a_0ac_k12 a_{0ac\\rightarrow k12} log10 1e-3 1e3 1.0 0
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a_0ac_k16 a_{0ac\\rightarrow k16} log10 1e-3 1e3 1.0 0
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a_k05_k05k08 a_{k05\\rightarrow k05k08} log10 1e-3 1e3 1.0 0
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a_k05_k05k12 a_{k05\\rightarrow k05k12} log10 1e-3 1e3 30.88820217382275 1
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a_k05_k05k16 a_{k05\\rightarrow k05k16} log10 1e-3 1e3 1.0 0
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a_k08_k05k08 a_{k08\\rightarrow k05k08} log10 1e-3 1e3 1.0 0
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a_k08_k08k12 a_{k08\\rightarrow k08k12} log10 1e-3 1e3 1.0 0
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a_k08_k08k16 a_{k08\\rightarrow k08k16} log10 1e-3 1e3 1.0 0
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a_k12_k05k12 a_{k12\\rightarrow k05k12} log10 1e-3 1e3 8.267790285514408 1
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a_k12_k08k12 a_{k12\\rightarrow k08k12} log10 1e-3 1e3 1.0 0
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a_k12_k12k16 a_{k12\\rightarrow k12k16} log10 1e-3 1e3 1.0 0
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a_k16_k05k16 a_{k16\\rightarrow k05k16} log10 1e-3 1e3 1.0 0
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a_k16_k08k16 a_{k16\\rightarrow k08k16} log10 1e-3 1e3 1.0 0
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a_k16_k12k16 a_{k16\\rightarrow k12k16} log10 1e-3 1e3 10.424803031743464 1
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a_k05k08_k05k08k12 a_{k05k08\\rightarrow k05k08k12} log10 1e-3 1e3 1.0 0
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a_k05k08_k05k08k16 a_{k05k08\\rightarrow k05k08k16} log10 1e-3 1e3 1.0 0
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a_k05k12_k05k08k12 a_{k05k12\\rightarrow k05k08k12} log10 1e-3 1e3 4.872782281489833 1
21+
a_k05k12_k05k12k16 a_{k05k12\\rightarrow k05k12k16} log10 1e-3 1e3 1.0 0
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a_k05k16_k05k08k16 a_{k05k16\\rightarrow k05k08k16} log10 1e-3 1e3 1.0 0
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a_k05k16_k05k12k16 a_{k05k16\\rightarrow k05k12k16} log10 1e-3 1e3 1.0 0
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a_k08k12_k05k08k12 a_{k08k12\\rightarrow k05k08k12} log10 1e-3 1e3 1.0 0
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a_k08k12_k08k12k16 a_{k08k12\\rightarrow k08k12k16} log10 1e-3 1e3 1.0 0
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a_k08k16_k05k08k16 a_{k08k16\\rightarrow k05k08k16} log10 1e-3 1e3 1.0 0
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a_k08k16_k08k12k16 a_{k08k16\\rightarrow k08k12k16} log10 1e-3 1e3 1.0 0
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a_k12k16_k05k12k16 a_{k12k16\\rightarrow k05k12k16} log10 1e-3 1e3 1.0 0
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a_k12k16_k08k12k16 a_{k12k16\\rightarrow k08k12k16} log10 1e-3 1e3 33.037720591816104 1
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a_k05k08k12_4ac a_{k05k08k12\\rightarrow 4ac} log10 1e-3 1e3 1.0 0
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a_k05k08k16_4ac a_{k05k08k16\\rightarrow 4ac} log10 1e-3 1e3 1.0 0
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a_k05k12k16_4ac a_{k05k12k16\\rightarrow 4ac} log10 1e-3 1e3 1.0 0
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a_k08k12k16_4ac a_{k08k12k16\\rightarrow 4ac} log10 1e-3 1e3 53.801757067907324 1
34+
a_b a_b log10 1e-3 1e3 0.0667579590703576 1
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da_b da_b lin 1.0 0
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sigma sigma log10 1e-12 1e3 0.1 1
36+
sigma_ sigma_ log10 1e-3 1e3 1.0 0

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