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. PosnerEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1945)


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.

Key words

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



Development of BioNetFit has been supported by National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) grant R01GM111510 (RGP and WSH). EDM acknowledges support from the Laboratory-Directed Research and Development (LDRD) program at Los Alamos National Laboratory (LANL), which is operated for the National Nuclear Security Administration (NNSA) of the US Department of Energy (DOE) under contract DE-AC52-06NA25396. RS is grateful for support from the Los Alamos Center for Nonlinear Studies (CNLS). KEE is supported by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by DOE and the National Cancer Institute (NCI) of NIH. JL acknowledges support from the Science, Technology, Engineering, and Mathematics Talent Expansion Program (STEP) of the National Science Foundation (NSF). M del C RÁ acknowledges support from the DOE/NNSA-sponsored Minority Serving Institutions (MSI) Internship program. The Monsoon cluster at Northern Arizona University (NAU) is supported by the Arizona Board of Regents Technology and Research Initiative Fund (TRIF). The Darwin cluster at Los Alamos National Laboratory is supported by the Computational Systems and Software Environment (CSSE) subprogram of the Advanced Simulation and Computing (ASC) program at LANL, which is funded by DOE/NNSA.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • William S. Hlavacek
    • 4
  • Jennifer A. Csicsery-Ronay
    • 1
  • Lewis R. Baker
    • 2
    • 3
  • María del Carmen Ramos Álamo
    • 4
  • Alexander Ionkov
    • 1
  • Eshan D. Mitra
    • 4
  • Ryan Suderman
    • 1
    • 5
  • Keesha E. Erickson
    • 4
  • Raquel Dias
    • 6
  • Joshua Colvin
    • 6
  • Brandon R. Thomas
    • 6
  • Richard G. Posner
    • 6
    Email author
  1. 1.Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear StudiesLos Alamos National LaboratoryLos AlamosUSA
  2. 2.Theoretical DivisionLos Alamos National LaboratoryLos AlamosUSA
  3. 3.Department of Applied MathematicsUniversity of ColoradoBoulderUSA
  4. 4.Theoretical Biology and Biophysics Group, Theoretical DivisionLos Alamos National LaboratoryLos AlamosUSA
  5. 5.Immunetrics, Inc.PittsburghUSA
  6. 6.Department of Biological SciencesNorthern Arizona UniversityFlagstaffUSA

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