  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.
