The pycotools.model.Model class

The pycotools3 tasks module contains classes for a bunch of copasi tasks that can be configured from python using pycotools. To simplify some of these tasks, wrappers have been build around these task classes in the model.Model class so that they can be used like a regular method. Here I demonstrate some of these.

We first configure a model for the demonstration

import os, glob
import pandas, numpy
import matplotlib.pyplot as plt
import seaborn
from pycotools3 import model, tasks, viz
seaborn.set_context(context='talk')

## Choose a directory for our model and analysis
working_directory = os.path.abspath('')

## In this model, A gets reversibly converted to B but the backwards reaction is additionally regulated by C.
## B is reversibly converted into C.
antimony_string = """
model simple_parameter_estimation()
    compartment Cell = 1;

    A in Cell;
    B in Cell;
    C in Cell;

    // reactions
    R1: A => B ; Cell * k1 * A;
    R2: B => A ; Cell * k2 * B * C;
    R3: B => C ; Cell * k3 * B;
    R4: C => B ; Cell * k4 * C;

    // initial concentrations
    A = 100;
    B = 1;
    C = 1;

    // reaction parameters
    k1 = 0.1;
    k2 = 0.1;
    k3 = 0.1;
    k4 = 0.1;
end
"""

copasi_file = os.path.join(working_directory, 'example_model.cps')

## build model
mod = model.loada(antimony_string, copasi_file)

assert isinstance(mod, model.Model)

Inserting parameters

dct = {
  'k1': 55,
  'k2': 36
}
mod.insert_parameters(parameter_dict=dct, inplace=True)

or

mod = mod.insert_parameters(parameter_dict=dct)

or

import pandas
df = pandas.DataFrame(dct, index=[0])
mod.insert_parameters(df=df, inplace=True)

or if the dataframe df has more than one parameter set we can specify the rank using the index argument.

import pandas
##insert second best parameter set
mod.insert_parameters(df=df, inplace=True, index=1)

Note

This is most useful when using viz.Parse output dataframes, which are pandas.DataFrame objects containing parameters in the columns and parameter sets in the rows, sorted by best RSS

or, assuming the variable results_directory is a directory to a folder containing parameter estimation results.

mod.insert_parameters(parameter_path=results_directory, inplace=True)

Simulating a time course

data = mod.simulate(0, 10, 11)

Simulates a deterministic time course, 11 time points between 0 and 10. data contains a pandas.DataFrame object with variables along the columns and time points down the rows.

fname = os.path.join(os.path.dirname(__file__), 'simulation_data.csv')
## write data to file named fname
data = mod.simulate(0, 10, 11, report_name=fname)

Like with the other shortcuts, arguments for the tasks.TimeCourse class are pass on.

data = mod.simulate(0, 10, 11, method='direct')

fname = ps.path.join(os.path.dirname(__file__), 'scan_results.csv')
mod.scan(variable='A', minimum=5, maximum=10, report_name=fname)

By default the scan type is set to ‘scan’. We can change this

fname = ps.path.join(os.path.dirname(__file__), 'scan_results.csv')
mod.simulate(0, 10, 11, method='direct', run_mode=False)
mod.scan(variable='A', scan_type='repeat',
         number_of_steps=10, report_name=fname,
         subtask='timecourse')

Note

In the mod.simulate we configure copasi to run a stochastic time course but do not execute. We then configure the repeat scan task to run the stochastic time course 10 times.

Sensitivities

sens = mod.sensitivities(
            subtask='steady_state', cause='all_parameters',
            effect='all_variables'
       )