BioMASS core functions (`biomass.core`)

BioMASS core functions

class biomass.core.Model(pkg_name)

Class for BioMASS model construction.

pkg_name

Path (dot-sepalated) to a biomass model directory. Use __package__.

Type:: str

create(show_info=False)

Build a biomass model.

Parameters:: show_info (bool (default: False)) – Set to True to print the information related to model size.
Returns:: model – The BioMASS model object.
Return type:: biomass.model_object.ModelObject

Examples

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

biomass.core.create_model(pkg_name, show_info=False)

Create a BioMASS model.

Parameters:

pkg_name (str) – Path (dot-sepalated) to a biomass model directory.
show_info (bool (default: False)) – Set to True to print the information related to model size.

Returns:

model – The BioMASS model object.

Return type:

biomass.model_object.ModelObject

Examples

>>> from biomass import create_model
>>> import your_model
>>> model = create_model(your_model.__package__)

biomass.core.optimize(model, x_id, *, disp_here=False, overwrite=False, optimizer_options=None)

Estimate model parameters from experimental data.

Parameters:

model (ModelObject) – Model for parameter estimation.
x_id (int) – Index of parameter set to estimate.
disp_here (bool (default: False)) – Whether to show the evaluated objective at every iteration.
overwrite (bool (default: False)) – If True, the directory (x_id/) will be overwritten.
optimizer_options (dict, optional) – Keyword arguments to pass to scipy.optimize.differential_evolution. For details, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html.

Return type:

None

Examples

>>> from biomass import create_model, optimize
>>> from biomass.models import copy_to_current
>>> copy_to_current("Nakakuki_Cell_2010")
>>> model = create_model("Nakakuki_Cell_2010")
>>> optimize(model, x_id=1)

Notes

Set simulation conditions and the corresponding experimental data in observable.py
Define an objective function to be minimized (objective()) in problem.py
Set lower/upper bounds of parameters to be estimated in search_param.py

biomass.core.run_analysis(model, *, target, metric='integral', create_metrics=None, style='barplot', show_progress=True, clustermap_kws=None, cbar_ax_tick_params=None, options=None)

Employ 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.

Parameters:

model (ModelObject) – Model for sensitivity analysis.
target (Literal["reaction", "parameter", "initial_condition"]) – Where to add a small perturbation to calculate sensitivity coefficients.
metric (str (default: 'integral')) – A word to specify the signaling metric.
create_metrics (Dict[str, Callable[[np.ndarray], Union[int, float]]], optional) – Create user-defined signaling metrics.
style (Literal["barplot", "heatmap"] (default: 'barplot')) –
- ‘barplot’
- ’heatmap’
show_progress (bool (default: True)) – Set to True to show the progress indicator while calculating sensitivity coefficients.
clustermap_kws (dict, optional) – Keyword arguments to pass to seaborn.clustermap() when style is ‘heatmap’.
cbar_ax_tick_params (dict, optional) – Keyword arguments to pass to ax.tick_params() of the color bar object when style is ‘heatmap’.
options (dict, optional) –
- show_indicesbool (default: True)
  (target == ‘reaction’) Set to True to put reaction index on each bar.
- excluded_paramslist of strings
  (target == ‘parameter’) List of parameters which are not used for analysis.
- excluded_initialslist of strings
  (target == ‘initial_condition’) List of species which are not used for analysis.
- overwritebool (default: True)
  If True, the sensitivity_coefficients/{target}/{metric}.npy file will be overwritten.

Return type:

None

Examples

>>> from biomass import create_model, run_analysis
>>> from biomass.models import copy_to_current
>>> copy_to_current("Nakakuki_Cell_2010")
>>> model = create_model("Nakakuki_Cell_2010")

Parameters

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

Initial condition

>>> run_analysis(model, target='initial_condition')

Reaction

>>> run_analysis(model, target='reaction')

biomass.core.run_simulation(model, *, viz_type='original', show_all=False, stdev=False)

Simulate ODE model with estimated parameter values.

Parameters:

model (ModelObject) – Model for simulation.
viz_type (str) –
- ‘average’:
  The average of simulation results with parameter sets in out/.
- ’best’:
  The best simulation result in out/, simulation with best_fit_param.
- ’original’:
  Simulation with the default parameters and initial values defined in set_model.py.
- ’n(=1,2,…)’:
  Use the parameter set in out/n/.
- ’experiment’
  Draw the experimental data written in observable.py without simulation results.
show_all (bool (default: False)) – Whether to show all simulation results.
stdev (bool (default: False)) – If True, the standard deviation of simulated values will be shown (only available for ‘average’ visualization type).

Return type:

None

Examples

>>> from biomass import create_model, run_simulation
>>> from biomass.models import copy_to_current
>>> copy_to_current("Nakakuki_Cell_2010")
>>> model = create_model("Nakakuki_Cell_2010")
>>> run_simulation(
...     model,
...     viz_type='average',
...     show_all=False,
...     stdev=True,
... )

BioMASS core functions (biomass.core)

BioMASS core functions (`biomass.core`)