parameter_estimation_multiple_models.rst

Parameter estimation with multiple models

This is an example of how to configure a parameter estimation for multiple COPASI models using pycotools. We first create two similar but different toy models for demonstration, then simulate some experimental data from one of them and and fit it back to both models.

import os, glob
import pandas, numpy
import matplotlib.pyplot as plt
import seaborn
from pycotools3 import model, tasks, viz

# set seaborn context
seaborn.set_context(context='talk')

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

model1_string = """
model model1()

    R1:   => A ; k1*S;
    R2: A =>   ; k2*A;
    R3:   => B ; k3*A;
    R4: B =>   ; k4*B*C; //feedback term
    R5:   => C ; k5*B;
    R6: C =>   ; k6*C;

    S = 1;
    k1 = 0.1;
    k2 = 0.1;
    k3 = 0.1;
    k4 = 0.1;
    k5 = 0.1;
    k6 = 0.1;
end
"""

model2_string = """
model model2()
    R1:   => A ; k1*S;
    R2: A =>   ; k2*A*C; //feedback term
    R3:   => B ; k3*A;
    R4: B =>   ; k4*B;
    R5:   => C ; k5*B;
    R6: C =>   ; k6*C;

    S = 1;
    k1 = 0.1;
    k2 = 0.1;
    k3 = 0.1;
    k4 = 0.1;
    k5 = 0.1;
    k6 = 0.1;
end
"""
# create paths to where we want the two models
copasi_file1 = os.path.join(working_directory, 'model1.cps')
copasi_file2 = os.path.join(working_directory, 'model2.cps')

# Assemble into lists
antimony_strings = [model1_string, model2_string]
copasi_files = [copasi_file1, copasi_file2]

# create models
model_list = []
for i in range(len(copasi_files)):
    model_list.append(model.loada(antimony_strings[i], copasi_files[i])

## simulate some data, returns a pandas.DataFrame
data = model_list[0].simulate(0, 20, 1)

## write data to file
experiment_filename = os.path.join(working_directory, 'data_from_model1.txt')
data.to_csv(experiment_filename)

# Create the context, passing the model list rather than the Model object
with tasks.ParameterEstimation.Context(model_list, experiment_filename, context='s', parameters='g') as context:
    context.set('separator', ',')
    context.set('run_mode', True)
    context.set('randomize_start_values', True)
    context.set('method', 'genetic_algorithm')
    context.set('population_size', 25)
    context.set('lower_bound', 1e-1)
    context.set('upper_bound', 1e1)

    config = context.get_config()

# Do the parameter estimation
pe = tasks.ParameterEstimation(config)

# Parse the resulting data
data = viz.Parse(pe).data
print(data)