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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 2019-09, Vol.19 (C), p.1012-1036 |
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Main Authors: | , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | 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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ISSN: | 2589-0042 2589-0042 |
DOI: | 10.1016/j.isci.2019.08.045 |