What's Changed

• State transition by @himoto in https://github.com/biomass-dev/biomass/pull/169
• Generalisation of mass action kinetics by @formersbach in https://github.com/biomass-dev/biomass/pull/172
• Drop support for old template by @himoto in https://github.com/biomass-dev/biomass/pull/176
• Fix typo in tutorial by @himoto in https://github.com/biomass-dev/biomass/pull/178
• Fix module name: model_object by @himoto in https://github.com/biomass-dev/biomass/pull/181
• Transfer graphing from BioMass to Text2Model by @formersbach in https://github.com/biomass-dev/biomass/pull/177
• Update docs to be compatible with the newest features by @himoto in https://github.com/biomass-dev/biomass/pull/182
• Fix f-string in is_phosphorylated rule by @himoto in https://github.com/biomass-dev/biomass/pull/184
• Refactor code for sensitivity analysis by @himoto in https://github.com/biomass-dev/biomass/pull/186
• Redirect rules by @himoto in https://github.com/biomass-dev/biomass/pull/188
• Fix issue #189 by @formersbach in https://github.com/biomass-dev/biomass/pull/190
• Fix docstring by @himoto in https://github.com/biomass-dev/biomass/pull/191
• Fixes #192 by @formersbach in https://github.com/biomass-dev/biomass/pull/193
• Update graphing tutorial to new graph function. by @formersbach in https://github.com/biomass-dev/biomass/pull/195

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.9.1...v0.10.0

What's Changed

• Enable running biomass without Graphviz by @himoto in https://github.com/biomass-dev/biomass/pull/162

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.9.0...v0.9.1

What's Changed

• Adding interactive plotting option. by @formersbach in https://github.com/biomass-dev/biomass/pull/144
• Changed how graph is passed to pygraphvis, fixing issue #147 by @formersbach in https://github.com/biomass-dev/biomass/pull/148
• Add graph visualization tutorial, resolves #145 by @formersbach in https://github.com/biomass-dev/biomass/pull/149
• [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in https://github.com/biomass-dev/biomass/pull/146
• Fixes documentation and changes default save directory of graph.py by @formersbach in https://github.com/biomass-dev/biomass/pull/154
• Remove exec by @himoto in https://github.com/biomass-dev/biomass/pull/157

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.8.0...v0.9.0

What's Changed

• Graph representation of biological model by @formersbach in https://github.com/biomass-dev/biomass/pull/131
• Drop support of Python <= 3.7 by @himoto in https://github.com/biomass-dev/biomass/pull/132
• Update graph.py by @himoto in https://github.com/biomass-dev/biomass/pull/133
• From text to model by @himoto in https://github.com/biomass-dev/biomass/pull/136
• Release v0.8.0 by @himoto in https://github.com/biomass-dev/biomass/pull/138
• Fix error in multi_observables_options by @himoto in https://github.com/biomass-dev/biomass/pull/140
• Fix dead link by @himoto in https://github.com/biomass-dev/biomass/pull/142

New Contributors

• @formersbach made their first contribution in https://github.com/biomass-dev/biomass/pull/131

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.7.2...v0.8.0

What's Changed

• Make error messages more user friendly by @himoto in https://github.com/biomass-dev/biomass/pull/123
• Fix #124 by @himoto in https://github.com/biomass-dev/biomass/pull/125
• Release v0.7.2 by @himoto in https://github.com/biomass-dev/biomass/pull/128

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.7.1...v0.7.2

What's Changed

• Create tutorial by @himoto in https://github.com/biomass-dev/biomass/pull/118
• Update logo by @himoto in https://github.com/biomass-dev/biomass/pull/119
• Release v0.7.1 by @himoto in https://github.com/biomass-dev/biomass/pull/121

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.7.0...v0.7.1

What's Changed

• Add support for Python 3.10 by @himoto in https://github.com/biomass-dev/biomass/pull/110
• Add CITATION.cff by @himoto in https://github.com/biomass-dev/biomass/pull/111
• Add InitialPopulation by @himoto in https://github.com/biomass-dev/biomass/pull/113
• Release v0.7.0 by @himoto in https://github.com/biomass-dev/biomass/pull/116

Renamed BioMASS modules:

| Old | New | | --- | ----- | | set_model.py | ode.py | | set_search_param.py | search_param.py | | fitness.py | problem.py |

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.6.2...v0.7.0

What's Changed

• Release v0.6.2 by @himoto in https://github.com/biomass-dev/biomass/pull/107

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.6.1...v0.6.2

What's Changed

• [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in https://github.com/biomass-dev/biomass/pull/97
• docs: git clone biomass by @himoto in https://github.com/biomass-dev/biomass/pull/98
• Add model of the G1/S cell cycle transition by @himoto in https://github.com/biomass-dev/biomass/pull/99
• Set 'LSODA' as default ODE solver by @himoto in https://github.com/biomass-dev/biomass/pull/100
• Add model for the proliferation–quiescence decision by @himoto in https://github.com/biomass-dev/biomass/pull/101
• Update example code for ExternalOptimizer by @himoto in https://github.com/biomass-dev/biomass/pull/103
• Release v0.6.1 by @himoto in https://github.com/biomass-dev/biomass/pull/104

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.6.0...v0.6.1

What's Changed

• Release v0.6.0 by @himoto in https://github.com/biomass-dev/biomass/pull/95
• [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in https://github.com/biomass-dev/biomass/pull/87

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.5.5...v0.6.0

What's Changed

• Fix error message for incorrect visualization options by @himoto in https://github.com/biomass-dev/biomass/pull/72
• Fix sensitivity analysis visualization problem by @himoto in https://github.com/biomass-dev/biomass/pull/81
• Add more tests by @himoto in https://github.com/biomass-dev/biomass/pull/83
• Release v0.5.5 by @himoto in https://github.com/biomass-dev/biomass/pull/84
• [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci in https://github.com/biomass-dev/biomass/pull/70

Full Changelog: https://github.com/biomass-dev/biomass/compare/v0.5.4...v0.5.5

• Docs:

  • Add simulation results for each model, see here

• Core functions:

  • Add method to check model indices

• Solver:

  • Update function to equilibrate a system (#68)

• Docs:

  • Fix several docstrings
  • Update references

• Requirements:

  • Set minimal numpy version to 1.17
  • Add typing_extensions

• Other:

  • Add link to BioMASS docker images on DockerHub

• Template

  • Update viz.py

    Use this template for creating models with biomass>=0.5.2

    from matplotlib import pyplot as plt
    
    from biomass.plotting import *
    
    from .observable import Observable
    
    
    class Visualization(Observable):
        """
        Plotting parameters for customizing figure properties.
    
        Attributes
        ----------
        cm : matplotlib.colors.ListedColormap (default: `plt.cm.get_cmap('tab10')`)
            Choosing colormaps for `cmap`.
        single_observable_options : list of SingleObservable
            Visualization options for time-course simulation (single-observable).
        multiple_observables_options : MultipleObservables
            Visualization options for time-course simulation (multi-observables).
        sensitivity_options : SensitivityOptions
            Visualization options for sensitivity analysis results.
        """
    
        def __init__(self):
            super().__init__()
    
            self.cm = plt.cm.get_cmap("tab10")
            self.single_observable_options = [
                SingleObservable(self.cm, obs_name) for obs_name in self.obs_names
            ]
            self.multiple_observables_options = MultipleObservables(self.cm)
            self.sensitivity_options = SensitivityOptions(self.cm)
    
        def get_single_observable_options(self):
    
            return self.single_observable_options
    
        def get_multiple_observables_options(self):
    
            return self.multiple_observables_options
    
        def get_sensitivity_options(self):
    
            return self.sensitivity_options
    
        @staticmethod
        def set_timecourse_rcParams():
            """figure/simulation"""
            plt.rcParams["font.size"] = 12
            plt.rcParams["axes.linewidth"] = 1.5
            plt.rcParams["xtick.major.width"] = 1.5
            plt.rcParams["ytick.major.width"] = 1.5
            plt.rcParams["lines.linewidth"] = 1.8
            plt.rcParams["lines.markersize"] = 12
            plt.rcParams["savefig.bbox"] = "tight"
            # plt.rcParams["savefig.format"] = "pdf"
            # plt.rcParams['font.family'] = 'Arial'
            # plt.rcParams['mathtext.fontset'] = 'custom'
            # plt.rcParams['mathtext.it'] = 'Arial:italic'
    
        @staticmethod
        def set_sensitivity_rcParams():
            """figure/sensitivity"""
            plt.rcParams["font.size"] = 12
            plt.rcParams["axes.linewidth"] = 1.2
            plt.rcParams["xtick.major.width"] = 1.2
            plt.rcParams["ytick.major.width"] = 1.2
            plt.rcParams["savefig.bbox"] = "tight"
            # plt.rcParams["savefig.format"] = "pdf"
            # plt.rcParams['font.family'] = 'Arial'
    
        @staticmethod
        def convert_species_name(name):
            """figure/sensitivity/initial_condition
            - Sensitivity for species with nonzero initial conditions
            """
            return name
    

• Core functions

  • Fix default workers in optimize function (#58)

• Parameter estimation

  • Fix a bug (#59)

• Other

  • Bump minimal sicpy version to 1.6
    • integrate.simps was renamed to integrate.simpson

• Template

  • Add new key: legend_kws
    • legend_loc becomes invalid
  • Several updates to default rcParams

• Core functions

  • Remove save_format from arguments in run_simulation and run_analysis
    • To modify save_format, edit viz.py in the model.
  • Use x_id to specify the index (indices) of parameter sets when running optimize
    • start and end become invalid
  • Add create_metrics : Dict[str, Callable[[np.ndarray], Union[int, float]]], optional to run_analysis
    • Allowing users to crete signaling metrics for sensitivity analysis

• Dependencies

  • Add pandas (See requirements.txt)

• Template

  • Update fitness.py

    • Create OptimizationProblem
    • Add bounds for external optimizer

  • Update viz.py

    • Add figsize (default: (4, 3)) to timecourse_options and multiplot_options

• biomass.result

  • Move visualization of estimated parameter sets to biomass.OptimizationResults.savefig()

    >>> from biomass import Model, OptimizationResults
    >>> from biomass.models import Nakakuki_Cell_2010
    >>> model = Model(Nakakuki_Cell_2010.__package__).create()
    >>> res = OptimizationResults(model)
    >>> res.savefig(figsize=(16,5), boxplot_kws={"orient": "v"})
    

    

• biomass.estimation

  • Create new class: ExternalOptimizer

    >>> from scipy.optimize import differential_evolution
    >>> from biomass import Model
    >>> from biomass.models import Nakakuki_Cell_2010
    >>> model = Model(Nakakuki_Cell_2010.__package__).create()
    >>> optimizer = ExternalOptimizer(model, differential_evolution)
    >>> res = optimizer.run(
    ...     model.problem.objective,
    ...     model.problem.bounds,
    ...     strategy="best2bin",
    ...     maxiter=100,
    ...     tol=1e-4,
    ...     mutation=0.1,
    ...     recombination=0.5,
    ...     disp=True,
    ...     polish=False,
    ...     workers=-1,
    ... )
    

    differential_evolution step 1: f(x)= 7.05589 differential_evolution step 2: f(x)= 5.59166 differential_evolution step 3: f(x)= 2.80301 ... differential_evolution step 100: f(x)= 0.538524

    >>> from biomass import run_simulation
    >>> optimizer.import_solution(res.x, x_id=0)
    >>> run_simulation(model, viz_type="0")
    

• Create docs: https://biomass-core.readthedocs.io/en/latest/

• Core functions

  • Update default values: https://biomass-core.readthedocs.io/en/latest/modules/core.html

• Optimization results

  • Set estimated params and initials to be plotted in one figure

• License

  • BioMASS >= 0.4 is released under the Apache License 2.0.

• Template

  • Don't use exec in name2idx

    Use make_dataclass instead.

    NAMES: List[str] = [...]
    
    NUM: int = len(NAMES)
    
    Parameters = make_dataclass(
        cls_name="Parameters",
        fields=[(name, int) for name in NAMES],
        namespace={"NAMES": NAMES, "NUM": NUM},
        frozen=True,
    )
    
    name2idx: Dict[str, int] = {k: v for v, k in enumerate(NAMES)}
    
    C = Parameters(**name2idx)
    
    del name2idx
    

• Model construction

  • Use Model class

    >>> from biomass import Model
    >>> import your_model
    >>> model = Model(your_model.__package__).create()
    

• Sensitivity analysis

  • Fix path to save sensitivity coefficients

    {your_model}/sensitivity_coefficients/{target}/{metric}.npy

• Other

  • Fix condition when exp.data to be plotted

  • Update signaling_systems.py

• Models

  • Add the insulin-dependent AKT pathway model

  • Reference:

    • Kubota, H. et al. Temporal Coding of Insulin Action through Multiplexing of the AKT Pathway. Mol. Cell 46, 820–832 (2012). https://doi.org/10.1016/j.molcel.2012.04.018

  • BioModels:

    • Kubota2012_InsulinAction_AKTpathway

• Template for BioMASS model construction

  • Update reaction_network.py

    from typing import Dict, List
    
    from biomass.analysis.reaction import is_duplicate
    
    
    class ReactionNetwork(object):
        """
        Reaction indices grouped according to biological processes.
        This is used for sensitivity analysis (target='reaction').
        """
    
        def __init__(self) -> None:
            self.reactions: Dict[str, List[int]] = {}
    
        def group(self):
            """
            Group reactions according to biological processes
            """
            biological_processes = []
            for process, indices in self.reactions.items():
                if not isinstance(indices, list):
                    raise TypeError("Use list for reaction indices in {}".format(process))
                biological_processes.append(indices)
    
            if not is_duplicate(self.reactions, biological_processes):
                return biological_processes
    
    

• Output

  • Update _write_best_fit_param.py

  • Example:

    # parameter set: 9
    
    
    def param_values():
        x = [0] * C.NUM
        x[C.V1] = 9.152e-02
        x[C.Km1] = 3.761e+02
        x[C.V2] = 2.200e-01
        x[C.Km2] = 3.500e+02
        x[C.V3] = 7.200e-01
        x[C.Km3] = 1.600e+02
        x[C.V4] = 6.480e-01
        x[C.Km4] = 6.000e+01
        x[C.V5] = 6.913e+01
        x[C.Km5] = 2.727e+01
        x[C.V6] = 6.913e+01
        x[C.Km6] = 2.727e+01
        x[C.KimERK] = 1.200e-02
        x[C.KexERK] = 1.800e-02
        x[C.KimpERK] = 1.200e-02
        x[C.KexpERK] = 1.800e-02
        x[C.KimppERK] = 1.100e-02
        x[C.KexppERK] = 1.300e-02
        x[C.V10] = 4.275e+02
        x[C.Km10] = 1.469e+01
        x[C.n10] = 2.593e+00
        x[C.p11] = 2.191e-04
        x[C.p12] = 1.156e-03
        x[C.p13] = 6.951e-04
        x[C.V14] = 3.090e-01
        x[C.Km14] = 8.111e+02
        x[C.V15] = 2.049e+02
        x[C.Km15] = 7.803e-03
        x[C.p16] = 2.570e-04
        x[C.p17] = 9.630e-05
        x[C.KimDUSP] = 1.832e-03
        x[C.KexDUSP] = 4.328e-02
        x[C.KimpDUSP] = 1.832e-03
        x[C.KexpDUSP] = 4.328e-02
        x[C.V20] = 3.512e-01
        x[C.Km20] = 1.321e+05
        x[C.V21] = 5.249e-02
        x[C.Km21] = 1.184e-03
        x[C.p22] = 2.570e-04
        x[C.p23] = 9.630e-05
        x[C.V24] = 3.440e+00
        x[C.Km24] = 1.912e+05
        x[C.V25] = 1.109e+02
        x[C.Km25] = 4.527e+00
        x[C.KimRSK] = 2.519e-03
        x[C.KexRSK] = 1.024e-02
        x[C.V27] = 1.390e+01
        x[C.Km27] = 4.619e+03
        x[C.V28] = 4.295e-01
        x[C.Km28] = 1.165e+00
        x[C.V29] = 9.103e-02
        x[C.Km29] = 6.333e+03
        x[C.V30] = 5.958e+01
        x[C.Km30] = 7.801e+00
        x[C.V31] = 5.691e+01
        x[C.Km31] = 1.543e-02
        x[C.n31] = 1.713e+00
        x[C.p32] = 1.100e-03
        x[C.p33] = 1.959e-03
        x[C.p34] = 7.572e-04
        x[C.V35] = 5.050e+00
        x[C.Km35] = 1.795e+03
        x[C.V36] = 1.218e-01
        x[C.Km36] = 2.394e+03
        x[C.V37] = 1.178e+03
        x[C.Km37] = 1.062e+02
        x[C.p38] = 2.570e-04
        x[C.p39] = 9.630e-05
        x[C.KimFOS] = 1.205e-01
        x[C.KexFOS] = 1.590e-01
        x[C.KimpcFOS] = 1.205e-01
        x[C.KexpcFOS] = 1.590e-01
        x[C.V42] = 9.649e-02
        x[C.Km42] = 5.639e+02
        x[C.V43] = 3.568e-03
        x[C.Km43] = 1.382e+02
        x[C.V44] = 1.290e-03
        x[C.Km44] = 2.725e-02
        x[C.p45] = 2.570e-04
        x[C.p46] = 9.630e-05
        x[C.p47] = 2.140e-01
        x[C.m47] = 1.288e+01
        x[C.p48] = 3.351e-02
        x[C.p49] = 4.279e-02
        x[C.m49] = 2.331e+00
        x[C.p50] = 3.491e-02
        x[C.p51] = 1.053e-03
        x[C.m51] = 4.536e+00
        x[C.p52] = 2.140e-01
        x[C.m52] = 1.288e+01
        x[C.p53] = 3.351e-02
        x[C.p54] = 4.279e-02
        x[C.m54] = 2.331e+00
        x[C.p55] = 3.491e-02
        x[C.p56] = 1.053e-03
        x[C.m56] = 4.536e+00
        x[C.V57] = 2.366e+02
        x[C.Km57] = 7.976e-04
        x[C.n57] = 1.649e+00
        x[C.p58] = 2.471e-09
        x[C.p59] = 2.889e-06
        x[C.p60] = 6.575e-02
        x[C.p61] = 2.517e+00
        x[C.KimF] = 2.058e-02
        x[C.KexF] = 7.998e-02
        x[C.p63] = 1.731e+00
        x[C.KF31] = 3.337e-03
        x[C.nF31] = 1.001e+00
        x[C.a] = 3.380e+02
        x[C.Vn] = 2.200e-01
        x[C.Vc] = 9.400e-01
        x[C.Ligand] = 0.000e+00
        x[C.EGF] = 0.000e+00
        x[C.HRG] = 1.000e+00
        x[C.no_ligand] = 2.000e+00
    
    
    def initial_values():
        y0 = [0] * V.NUM
        y0[V.CREBn] = 1.000e+03
        y0[V.ERKc] = 9.600e+02
        y0[V.Elk1n] = 1.510e+03
        y0[V.RSKc] = 3.530e+02
    

• Visualization

  • Close #45

• Other

  • Update function to get the steady state for the untreated condition

    • Use scipy.integrate.ode

  • Use math.isnan for single floats

  • code refactoring

    • analysis/

    • estimation/

Updates:

Parameter estimation

• Fix search bounds, mutation and popsize in the differential evolution method.

Models

• Move ODE solvers to dynamics/
• Move search_param checkers to estimation/

Test

• Update test paramter sets

Update optimize

• Add new option: initial_threshold

  • initial_threshold : float (default: 1e12)

  Threshold on objective function value used to generate initial population. Default value is 1e12 (numerically solvable).

def optimize(
    model: ModelObject,
    start: int,
    end: Optional[int] = None,
    options: Optional[dict] = None,
) -> None:
    """
    Run GA for parameter estimation.

    Paremters
    ---------
    model : ModelObject
        Model for parameter estimation.

    start : int
        Index of parameter set to estimate.

    end : int, optional
        When `end` is specified, parameter sets from `start` to `end` will be estimated.

    options : dict, optional
        popsize : int (default: 5)
            A multiplier for setting the total population size.
            The population has popsize * len(search_param) individuals.

        max_generation : int (default: 10000)
            Stop optimization if Generation > max_generation.

        initial_threshold : float (default: 1e12)
            Threshold on objective function value used to generate initial population.
            Default value is 1e12 (numerically solvable).

        allowable_error : float (default: 0.0)
            Stop optimization if Best Fitness <= allowable_error.

        local_search_method : str (default: 'mutation')
            Method used in local search. Should be one of
            * 'mutation' : NDM/MGG
            * 'Powell' : Modified Powell method
            * 'DE' : Differential Evolution (strategy: best2bin)

        n_children : int (default: 200)
            (method='mutation') The number of children generated in NDM/MGG.

        maxiter : int (default: 10)
            (method='Powell' or 'DE') The maximum number of iterations
            over which the entire population is evolved.

        workers : int (default: -1 if `end` is None else 1)
            (method='DE') The population is subdivided into workers sections and
            evaluated in parallel (uses multiprocessing.Pool). Supply -1 to use
            all available CPU cores. Set workers to 1 when searching multiple
            parameter sets simultaneously.

        overwrite : bool (default: False)
            If True, the out/n folder will be overwritten.

    Example
    -------
    >>> from biomass.models import Nakakuki_Cell_2010
    >>> from biomass import optimize
    >>> model = Nakakuki_Cell_2010.create()
    >>> optimize(
            model=model, start=1, end=10,
            options={
                'max_generation': 10000,
                'allowable_error': 0.5
            }
        )
    """
    ...

Update run_analysis

def run_analysis(
    model: ModelObject,
    target: str,
    metric: str = "integral",
    style: str = "barplot",
    save_format: str = "pdf",
    options: Optional[dict] = None,
) -> None:
    """
    Perform sensitivity analysis to identify critical parameters, species or
    reactions in the complex biological network.

    The sensitivity S(y,x) was calculated according to the following equation:
    S(y,x) = d ln(yi) / d ln (xj), where yi is the signaling metric and xj is
    each nonzero species, parameter value or reaction rate.

    Paremters
    ---------
    model : ModelObject
        Model for sensitivity analysis.

    target : str
        * 'reaction'
        * 'initial_condition'
        * 'parameter'

    metric : str (default: 'integral')
        * 'maximum' : The maximum value.
        * 'minimum' : The minimum value.
        * 'argmax' : The time to reach the maximum value.
        * 'argmin' : The time to reach the minimum value.
        * 'timepoint' : The simulated value at the time point set via options['timepoint'].
        * 'duration' :  The time it takes to decline below the threshold set via options['duration'].
        * 'integral' : The integral of concentration over the observation time.

    style : str (default: 'barplot')
        * 'barplot'
        * 'heatmap'

    save_format : str (default: "pdf")
        Either "png" or "pdf", indicating whether to save figures
        as png or pdf format.

    options : dict, optional
        show_indices : bool (default: True)
            (target == 'reaction') Set to True to put reaction index on each bar.

        excluded_params : list of strings
            (target == 'parameter') List of parameters which are not used for analysis.

        excluded_initials : list of strings
            (target == 'initial_condition') List of species which are not used for analysis.

        timepoint : int (default: model.sim.t[-1])
            (metric == 'timepoint') Which timepoint to use.

        duration : float (default: 0.5)
            (metric == 'duration') 0.1 for 10% of its maximum.

    Example
    -------
    >>> from biomass.models import Nakakuki_Cell_2010
    >>> from biomass import run_analysis
    >>> model = Nakakuki_Cell_2010.create()

    1. Parameter
    >>> run_analysis(
            model,
            target='parameter',
            options = {
                'excluded_params': [
                    'a', 'Vn', 'Vc', 'Ligand', 'EGF', 'HRG', 'no_ligand'
                ]
            }
        )

    2. Initial condition
    >>> run_analysis(
            model,
            target='initial_condition',
        )

    3. Reaction
    >>> run_analysis(
            model,
            target='reaction',
        )

    """
    ...

Coding style

Code is formatted with black. Coding style is checked with flake8 and isort.

$ pip install pre-commit

$ pre-commit install

Display model information

Rename function: ~~show_properties~~ → show_info

from biomass.models import Nakakuki_Cell_2010

Nakakuki_Cell_2010.show_info()

↓

Nakakuki_Cell_2010 information
------------------------------
36 species
115 parameters, of which 75 to be estimated

Add new method to OptimizationResults: trace_obj

Visualize objective function traces for different optimization runs.

from biomass.models import Nakakuki_Cell_2010
from biomass.result import OptimizationResults

model = Nakakuki_Cell_2010.create()
res = OptimizationResults(model)

res.trace_obj()

Updates:

New model

Dynamic pathway model of TNFα-induced NFκB signal transduction

Oppelt, A. et al. Model-based identification of TNFα-induced IKKβ-mediated and IκBα-mediated regulation of NFκB signal transduction as a tool to quantify the impact of drug-induced liver injury compounds. npj Syst. Biol. Appl. 4, 23 (2018). https://doi.org/10.1038/s41540-018-0058-z

Sensitivity analysis

New signaling metrics

• 'argmax' : The time to reach the maximum value.
• 'argmin' : The time to reach the minimum value.
• 'timepoint' : The simulated value at the time point set via options['timepoint'].
• 'duration' : The time it takes to decline below the threshold set via options['duration'].

Parameter estimation

• Set mutation constant (differential weight: F) to 0.1

• Set recombination constant (crossover probability: CR) to 0.9

Updates:

• Use BioMassModel for the first argument of core functions

Example usage

1. Create an executable model

   from biomass.models import Nakakuki_Cell_2010
   
   model = Nakakuki_Cell_2010.create()
   

2. Estimate model parameters from experimental data

   from biomass import optimize
   
   optimize(
       model=model, start=1, options={
           "popsize": 3,
           "max_generation": 1000,
           "allowable_error": 0.5,
           "local_search_method": "DE",
       }
   )
   

3. Export optimized parameters in CSV format

   from biomass.result import OptimizationResults
   
   res = OptimizationResults(model)
   res.to_csv()
   

4. Visualize simulation results

   from biomass import run_simulation
   
   run_simulation(model, viz_type='average', show_all=False, stdev=True)
   

5. Perform sensitivity analysis

   from biomass import run_analysis
   
   run_analysis(model, target='reaction', metric='integral', style='barplot')
   

• Small fixes

• Update ODE solver

  • From scipy.integrate.ode to scipy.integrate.solve_ivp

Updates:

Added new local search method: "Powell" and "DE"

def optimize(model, *args, options: Optional[dict] = None) -> None:
    """
    Run GA for parameter estimation.

    Paremters
    ---------
    model : module
        Model for parameter estimation.

    options: dict, optional
        popsize : int (default: 5)
            A multiplier for setting the total population size.
            The population has popsize * len(search_param) individuals.

        max_generation : int (default: 10000)
            Stop if Generation > max_generation.

        allowable_error : float (default: 0.0)
            Stop if Best Fitness <= allowable_error.

        local_search_method : str (default: 'mutation')
            Method used in local search. Should be one of
            - 'mutation' : NDM/MGG
            - 'Powell' : Modified Powell method
            - 'DE' : Differential Evolution (strategy: best2bin)

        n_children : int (default: 200)
            (method='mutation') The number of children generated in NDM/MGG.

        workers : int (default: -1 if len(args) == 1 else 1)
            (method='DE') The population is subdivided into workers sections and
            evaluated in parallel (uses multiprocessing.Pool). Supply -1 to use
            all available CPU cores. Set workers to 1 when searching multiple
            parameter sets simultaneously.

        overwrite : bool (default: False)
            If True, the out/n folder will be overwritten.
    """

Example usage

from biomass.models import Nakakuki_Cell_2010
from biomass import optimize

optimize(
    Nakakuki_Cell_2010, 1, options={
        'popsize': 3,
        'allowable_error': 0.5,
        'local_search_method': 'DE'
    }
)

Fix:

• result.py

Updates:

• Change sim.normalization type from bool to dict (nested):

  def __init__(self):
      super().__init__(perturbation={})
      self.normalization = {}
      for obs_name in observables:
          self.normalization[obs_name] = {
              'timepoint' : None,
              'condition' : []
          }
  

  • 'timepoint' : Optional[int]

    • The time point at which simulated values are normalized. If None, the maximum value will be used for normalization.

  • 'condition' : list of strings

    • The experimental conditions to use for normalization. If empty, all conditions defined in sim.conditions will be used.

• Move documentation to https://github.com/okadalabipr/biomass_docs.jl