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A Step-by-Step Guide to Using BioNetFit.
Hlavacek, William S; Csicsery-Ronay, Jennifer A; Baker, Lewis R; Ramos Álamo, María Del Carmen; Ionkov, Alexander; Mitra, Eshan D; Suderman, Ryan; Erickson, Keesha E; Dias, Raquel; Colvin, Joshua; Thomas, Brandon R; Posner, Richard G.
Afiliação
  • Hlavacek WS; Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Csicsery-Ronay JA; Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Baker LR; Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Ramos Álamo MDC; Department of Applied Mathematics, University of Colorado, Boulder, CO, USA.
  • Ionkov A; Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Mitra ED; Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Suderman R; Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Erickson KE; Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Dias R; Immunetrics, Inc., Pittsburgh, PA, USA.
  • Colvin J; Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Thomas BR; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.
  • Posner RG; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.
Methods Mol Biol ; 1945: 391-419, 2019.
Article em En | MEDLINE | ID: mdl-30945257
ABSTRACT
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional / Biologia de Sistemas / Modelos Biológicos Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Biologia Computacional / Biologia de Sistemas / Modelos Biológicos Idioma: En Ano de publicação: 2019 Tipo de documento: Article