@article{00049c604a3b4bd4ad88c02e75f91963,
title = "PyBioNetFit and the Biological Property Specification Language",
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.",
keywords = "Bioinformatics, Biological Sciences, Complex Systems, Computer Science, Parallel System, Systems Biology",
author = "Mitra, {Eshan D.} and Ryan Suderman and Joshua Colvin and Alexander Ionkov and Andrew Hu and Sauro, {Herbert M.} and Posner, {Richard G.} and Hlavacek, {William S.}",
note = "Funding Information: This work was supported by grant R01GM111510 from the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health (NIH). W.S.H. acknowledges support from the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of NIH. R.S. and A.I. acknowledge support from the Center for Nonlinear Studies at Los Alamos National Laboratory (LANL), which is operated for the National Nuclear Security Administration (NNSA) of the DOE under contract 89233218CNA000001 . H.M.S. acknowledges the support of grant R01GM123032 from NIGMS/NIH and grant P41EB023912 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of NIH. We thank J. Kyle Medley and Kiri Choi for assistance with libRoadRunner development. We thank Adrian Hauber for assistance with Data2Dynamics. Computational resources used in this study included the following: the Darwin cluster at LANL, which is supported by the Computational Systems and Software Environment (CSSE) subprogram of the Advanced Simulation and Computing (ASC) program at LANL, which is funded by NNSA /DOE; resources were provided by the LANL Institutional Computing program, which is funded by NNSA/DOE, and Northern Arizona University{\textquoteright}s Monsoon computer cluster, which is funded by Arizona{\textquoteright}s Technology and Research Initiative Fund. Publisher Copyright: {\textcopyright} 2019 The Author(s)",
year = "2019",
month = sep,
day = "27",
doi = "10.1016/j.isci.2019.08.045",
language = "English (US)",
volume = "19",
pages = "1012--1036",
journal = "iScience",
issn = "2589-0042",
publisher = "Elsevier Inc.",
}