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PyBioNetFit and the Biological Property Specification Language.
Mitra, Eshan D; Suderman, Ryan; Colvin, Joshua; Ionkov, Alexander; Hu, Andrew; Sauro, Herbert M; Posner, Richard G; Hlavacek, William S.
Afiliación
  • Mitra ED; Theoretical Biology and Biophysics Group, Theoretical Division, 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.
  • Colvin J; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.
  • Ionkov A; Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
  • Hu A; Department of Bioengineering, University of Washington, Seattle, WA, USA.
  • Sauro HM; Department of Bioengineering, University of Washington, Seattle, WA, USA.
  • Posner RG; Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.
  • Hlavacek WS; Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. Electronic address: wish@lanl.gov.
iScience ; 19: 1012-1036, 2019 Sep 27.
Article en En | MEDLINE | ID: mdl-31522114
ABSTRACT
In systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit, which in addition supports checking models against known system properties and solving design problems. PyBioNetFit introduces Biological Property Specification Language (BPSL) for the formal declaration of system properties. BPSL allows qualitative data to be used alone or in combination with quantitative data. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms that work directly with existing model definition standards BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit's capabilities by solving various example problems, including the challenging problem of parameterizing a 153-parameter model of cell cycle control in yeast based on both quantitative and qualitative data. We demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of targeted drug interventions in autophagy signaling.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Qualitative_research Idioma: En Revista: IScience Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Qualitative_research Idioma: En Revista: IScience Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos