Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Journal Article · Wed Jan 05 00:00:00 EST 2022 · Bioinformatics

DOI:https://doi.org/10.1093/bioinformatics/btac004· OSTI ID:1964184

Neumann, Jacob; Lin, Yen Ting ; Mallela, Abhishek ; Miller, Ely F.; Colvin, Joshua; Duprat, Abell T.; Chen, Ye; Hlavacek, William S. ; Posner, Richard G.; Kendziorski, ed., Christina

Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1964184
• Alternate ID(s): OSTI ID: 2202630
• Report Number(s): LA-UR-21-29283
• Journal Information: Bioinformatics, Journal Name: Bioinformatics Vol. 38 Journal Issue: 6; ISSN 1367-4803
• Publisher: Oxford University Press
• Country of Publication: United Kingdom
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
97 MATHEMATICS AND COMPUTING