SBstoat 1.0.2

Parameter fitting for SBML models

Fitting SBML Models with Tellurium

This repo provides easy-to-use tools for doing parameter using the Tellurium simulator. The project is named after the stoat, an animal that has superb skills at fitting into small places.

The main module is modelFitter. A typically parameter fitting session is shown below. For convenience, the model is expressed using the Antimony modeling language. However, SBML models can be loaded into tellurium, and a tellurium object can be used in place of the antimony model.

ANTIMONY_MODEL = """ 
# Reactions   
J1: S1 -> S2; k1*S1
J2: S2 -> S3; k2*S2
J3: S3 -> S4; k3*S3
J4: S4 -> S5; k4*S4
J5: S5 -> S6; k5*S5;
# Species initializations
S1 = 10; S2 = 0; S3 = 0; S4 = 0; S5 = 0; S6 = 0;
k1 = 1; k2 = 2; k3 = 3; k4 = 4; k5 = 5;
"""

Now suppose we have the data file tst_data.txt. To fit this model to these data and see a report on the fit:

# Fit parameters to ts1
from SBstoat.modelFitter import ModelFitter
fitter = ModelFitter(ANTIMONY_MODEL, "tst_data.txt", ["k1", "k2", "k3", "k4", "k5"])
fitter.fitModel()
print(fitter.reportFit())

The output is:

[Fit Statistics]]
# fitting method   = leastsq
# function evals   = 49
# data points      = 180
# variables        = 5
chi-square         = 73.2546170
reduced chi-square = 0.41859781
Akaike info crit   = -151.822803
Bayesian info crit = -135.858019

[[Variables]]
k1:  0.95579053 +/- 0.03816186 (3.99%) (init = 5)
k2:  2.24079567 +/- 0.19847112 (8.86%) (init = 5)
k3:  2.96763525 +/- 0.35879852 (12.09%) (init = 5)
k4:  3.07652723 +/- 0.39858904 (12.96%) (init = 5)
k5:  5.90802238 +/- 1.43620318 (24.31%) (init = 5)

[[Correlations]] (unreported correlations are < 0.100)
C(k4, k5) = -0.248
C(k3, k4) = -0.226
C(k2, k3) = -0.218
C(k3, k5) = -0.211
C(k2, k4) = -0.189
C(k1, k2) = -0.179
C(k2, k5) = -0.178
C(k1, k3) = -0.147
C(k1, k5) = -0.144
C(k1, k4) = -0.141

You can also get bootstrap estimates of parameter values. Because bootstrapping is computationally intensive, SBstoat uses multiple processes on your machine.

# Get estimates of parameters
fitter.bootstrap(numIteration=2000, reportInterval=500)
fitter.reportBootstrap()

Here is the output:

**Running bootstrap for 2000 iterations with 4 processes.
bootstrap completed 500 iterations.
bootstrap completed 1000 iterations.
bootstrap completed 1500 iterations.
Completed bootstrap process 2.
Completed bootstrap process 3.
Completed bootstrap process 4.
bootstrap completed 2000 iterations.
Completed bootstrap process 1.

Bootstrap Report.
Total iterations: 2000
Total simulation: 2000
k1
  mean: 0.9666458789599315
  std: 0.03984278523619386
  [2.5, 97.55] Percentiles: [0.89206257 1.04470717]
k2
  mean: 2.1808554007110637
  std: 0.17819579282363782
  [2.5, 97.55] Percentiles: [1.85917689 2.56348925]
k3
  mean: 3.233849345953018
  std: 0.4074066158009789
  [2.5, 97.55] Percentiles: [2.57874824 4.12921803]
k4
  mean: 3.1037923601143054
  std: 0.38872479522475384
  [2.5, 97.55] Percentiles: [2.46792396 4.06937082]
k5
  mean: 5.9285194938461565
  std: 1.0301263970600283
  [2.5, 97.55] Percentiles: [4.42373341 8.44386604]

More details of the features of SBstoat can be found in this tutorial.

Installation

1. pip install SBstoat

Developer Notes

1. run tests as follows:

   1. change to this directory

   2. set the environment variable PYTHONPATH to the absolute path of this directory.

      • Windows
      • Linux and Mac
        • PYTHONPATH=<current directory>
        • export PYTHONPATH

   3. nosetests tests