A Step-by-Step Guide to Using BioNetFit

William S. Hlavacek, Jennifer A. Csicsery-Ronay, Lewis R. Baker, María del Carmen Ramos Álamo, Alexander Ionkov, Eshan D. Mitra, Ryan Suderman, Keesha E. Erickson, Raquel Dias, Joshua Colvin, Brandon R. Thomas, Richard G. Posner

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations


BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitting (i.e., nonlinear regression). BioNetFit is compatible with deterministic and stochastic simulators that accept BioNetGen language (BNGL)-formatted files as inputs, such as those available within the BioNetGen framework. BioNetFit can be used on a laptop or stand-alone multicore workstation as well as on many Linux clusters, such as those that use the Slurm Workload Manager to schedule jobs. BioNetFit implements a metaheuristic population-based global optimization procedure, an evolutionary algorithm (EA), to minimize a user-defined objective function, such as a residual sum of squares (RSS) function. BioNetFit also implements a bootstrapping procedure for determining confidence intervals for parameter estimates. Here, we provide step-by-step instructions for using BioNetFit to estimate the values of parameters of a BNGL-encoded model and to define bootstrap confidence intervals. The process entails the use of several plain-text files, which are processed by BioNetFit and BioNetGen. In general, these files include (1) one or more EXP files, which each contains (experimental) data to be used in parameter identification/bootstrapping; (2) a BNGL file containing a model section, which defines a (rule-based) model, and an actions section, which defines simulation protocols that generate GDAT and/or SCAN files with model predictions corresponding to the data in the EXP file(s); and (3) a CONF file that configures the fitting/bootstrapping job and that defines algorithmic parameter settings.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Number of pages29
StatePublished - 2019

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


  • Confidence level
  • Genetic algorithm (GA)
  • Model calibration
  • Network-free simulation
  • Nonlinear least squares fitting
  • Ordinary differential equations (ODEs)
  • Parameter estimation
  • Parameter uncertainty
  • Rule-based modeling
  • Stochastic simulation algorithm (SSA)

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics


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