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PyBioNetFit and the Biological Property Specification Language

31 Pages Posted: 6 May 2019 Publication Status: Published

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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. PyBioNetFit is designed for parameterization, and also supports uncertainty quantification, checking models against known system properties, and solving design problems. PyBioNetFit introduces the 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 for parameterization model checking, and design. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms (differential evolution, particle swarm optimization, scatter search) that work directly with existing model definition standards: BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit’s capabilities by solving 31 example problems, including the challenging problem of parameterizing a model of cell cycle control in yeast. We benchmark PyBioNetFit’s parallelization efficiency on computer clusters, using up to 288 cores. Finally, we demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of therapeutic interventions in autophagy signaling.

Keywords: categorical data, formal verification, parallel computing, parameter identification, rule-based modeling

Suggested Citation

Mitra, Eshan D. and Suderman, Ryan and Colvin, Joshua and Ionkov, Alexander and Hu, Andrew and Sauro, Herbert M. and Posner, Richard G. and Hlavacek, William, PyBioNetFit and the Biological Property Specification Language (May 4, 2019). Available at SSRN: https://ssrn.com/abstract=3382545 or http://dx.doi.org/10.2139/ssrn.3382545
This version of the paper has not been formally peer reviewed.

Eshan D. Mitra

Independent

Ryan Suderman

Independent

Joshua Colvin

Independent

Alexander Ionkov

Independent

Andrew Hu

Independent

Herbert M. Sauro

Independent

Richard G. Posner

Independent