TY - CHAP
T1 - A Step-by-Step Guide to Using BioNetFit
AU - Hlavacek, William S.
AU - Csicsery-Ronay, Jennifer A.
AU - Baker, Lewis R.
AU - Ramos Álamo, María del Carmen
AU - Ionkov, Alexander
AU - Mitra, Eshan D.
AU - Suderman, Ryan
AU - Erickson, Keesha E.
AU - Dias, Raquel
AU - Colvin, Joshua
AU - Thomas, Brandon R.
AU - Posner, Richard G.
N1 - Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Confidence level
KW - Genetic algorithm (GA)
KW - Model calibration
KW - Network-free simulation
KW - Nonlinear least squares fitting
KW - Ordinary differential equations (ODEs)
KW - Parameter estimation
KW - Parameter uncertainty
KW - Rule-based modeling
KW - Stochastic simulation algorithm (SSA)
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U2 - 10.1007/978-1-4939-9102-0_18
DO - 10.1007/978-1-4939-9102-0_18
M3 - Chapter
C2 - 30945257
AN - SCOPUS:85064239289
T3 - Methods in Molecular Biology
SP - 391
EP - 419
BT - Methods in Molecular Biology
PB - Humana Press Inc.
ER -