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arxiv: 1903.07750 · v1 · pith:YB43SAHWnew · submitted 2019-03-18 · 🧬 q-bio.QM

PyBioNetFit and the Biological Property Specification Language

classification 🧬 q-bio.QM
keywords pybionetfitmodellanguageparameterizationbpslcheckingdesignbiological
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Parameter Estimation and Uncertainty Quantification for Systems Biology Models

    q-bio.QM 2019-06 unverdicted novelty 2.0

    Review of gradient-based and gradient-free methods for parameter point estimation plus profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification in systems biology models.