PyBioNetFit and the Biological Property Specification Language
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 s...
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| Published in | iScience Vol. 19; no. C; pp. 1012 - 1036 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
27.09.2019
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2589-0042 2589-0042 |
| DOI | 10.1016/j.isci.2019.08.045 |
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| 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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•PyBioNetFit is a software tool for parameterizing systems biology models•PyBioNetFit has support for uncertainty quantification, model checking, and design•BPSL enables formulation of qualitative system properties to use in fitting•Example problems are demonstrated on single workstations and on computer clusters
Biological Sciences; Bioinformatics; Systems Biology; Complex Systems; Computer Science; Parallel System |
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| AbstractList | 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. 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. [Display omitted] •PyBioNetFit is a software tool for parameterizing systems biology models•PyBioNetFit has support for uncertainty quantification, model checking, and design•BPSL enables formulation of qualitative system properties to use in fitting•Example problems are demonstrated on single workstations and on computer clusters Biological Sciences; Bioinformatics; Systems Biology; Complex Systems; Computer Science; Parallel System 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. • PyBioNetFit is a software tool for parameterizing systems biology models • PyBioNetFit has support for uncertainty quantification, model checking, and design • BPSL enables formulation of qualitative system properties to use in fitting • Example problems are demonstrated on single workstations and on computer clusters Biological Sciences; Bioinformatics; Systems Biology; Complex Systems; Computer Science; Parallel System 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.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. 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. : Biological Sciences; Bioinformatics; Systems Biology; Complex Systems; Computer Science; Parallel System Subject Areas: Biological Sciences, Bioinformatics, Systems Biology, Complex Systems, Computer Science, Parallel System |
| Author | Ionkov, Alexander Colvin, Joshua Sauro, Herbert M. Posner, Richard G. Hlavacek, William S. Mitra, Eshan D. Suderman, Ryan Hu, Andrew |
| AuthorAffiliation | 1 Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA 2 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA 3 Department of Bioengineering, University of Washington, Seattle, WA, USA |
| AuthorAffiliation_xml | – name: 3 Department of Bioengineering, University of Washington, Seattle, WA, USA – name: 1 Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA – name: 2 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA |
| Author_xml | – sequence: 1 givenname: Eshan D. surname: Mitra fullname: Mitra, Eshan D. organization: Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA – sequence: 2 givenname: Ryan surname: Suderman fullname: Suderman, Ryan organization: Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA – sequence: 3 givenname: Joshua surname: Colvin fullname: Colvin, Joshua organization: Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA – sequence: 4 givenname: Alexander surname: Ionkov fullname: Ionkov, Alexander organization: Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA – sequence: 5 givenname: Andrew surname: Hu fullname: Hu, Andrew organization: Department of Bioengineering, University of Washington, Seattle, WA, USA – sequence: 6 givenname: Herbert M. surname: Sauro fullname: Sauro, Herbert M. organization: Department of Bioengineering, University of Washington, Seattle, WA, USA – sequence: 7 givenname: Richard G. surname: Posner fullname: Posner, Richard G. organization: Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA – sequence: 8 givenname: William S. orcidid: 0000-0003-4383-8711 surname: Hlavacek fullname: Hlavacek, William S. email: wish@lanl.gov organization: Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31522114$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/1561348$$D View this record in Osti.gov |
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