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A Step-by-Step Guide to Using BioNetFit.

William S Hlavacek1, Jennifer A Csicsery-Ronay2, Lewis R Baker3,4

  • 1Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.

Methods in Molecular Biology (Clifton, N.J.)
|April 5, 2019
PubMed
Summary
This summary is machine-generated.

BioNetFit is a new software tool that uses evolutionary algorithms for parameter identification in rule-based models. It aids in estimating parameter values and defining confidence intervals for biological network models.

Keywords:
Confidence levelGenetic algorithm (GA)Model calibrationNetwork-free simulationNonlinear least squares fittingOrdinary differential equations (ODEs)Parameter estimationParameter uncertaintyRule-based modelingStochastic simulation algorithm (SSA)

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Rule-based modeling is crucial for understanding complex biological systems.
  • Parameter identification is a key challenge in developing accurate rule-based models.
  • Existing tools may lack integrated solutions for parameter estimation and confidence interval determination.

Purpose of the Study:

  • To introduce BioNetFit, a software tool for parameter identification in rule-based models.
  • To provide a comprehensive workflow for estimating model parameters and confidence intervals using BioNetFit.
  • To demonstrate the utility of BioNetFit with deterministic and stochastic simulators compatible with BioNetGen language (BNGL).

Main Methods:

  • Utilizes curve fitting (nonlinear regression) for parameter identification.
  • Implements a population-based global optimization evolutionary algorithm (EA) to minimize objective functions (e.g., residual sum of squares).
  • Incorporates a bootstrapping procedure for calculating confidence intervals of parameter estimates.

Main Results:

  • BioNetFit successfully estimates parameter values for BNGL-encoded models.
  • The tool enables the definition of bootstrap confidence intervals for parameter estimates.
  • The workflow involves processing EXP, BNGL, and CONF files for fitting and bootstrapping jobs.

Conclusions:

  • BioNetFit offers a robust solution for parameter identification in rule-based modeling.
  • The software is compatible with various simulation platforms and computational resources.
  • Provides a user-friendly, step-by-step approach for model parameterization and uncertainty quantification.