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

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.

Differential contagiousness of respiratory disease across the United States

“…We focused on early surveillance data (vs. all available surveillance data up to the present time) so as to characterize COVID-19 transmission within populations that are nearly wholly susceptible. Markov Chain Monte Carlo (MCMC) sampling was performed using the Python code of Lin et al (11) and a new release of PyBioNetFit (21) , version 1.1.9, which includes an implementation of the adaptive MCMC method used in the study of Lin et al (11) . Inference job setup files for PyBioNetFit, including data files, are provided for each of 280 MSAs (Data S1).…”

“…For each of these MSAs, we applied a Bayesian inference approach described earlier (11, 20) , which is enabled by an adaptive Markov chain Monte Carlo (MCMC) sampling procedure. Inference job setup files for PyBioNetFit (21) , including files with MSA-specific surveillance data, are provided for each of the 280 MSAs (Data S1). To ensure that MCMC sampling converged, we visually inspected log-likelihood trace plots, parameter trace plots, and pairs plots.…”

Quantification of early nonpharmaceutical interventions aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah)

“…MCMC sampling was performed to obtain samples of the parameter posterior. We used an adaptive MCMC sampling algorithm described earlier [22] and implemented in the PyBioNetFit software package [23]. PyBioNetFit job setup files for the inferences performed in this study, including data files, are available online (https://github.com/lanl/PyBNF/tree/master/examples/Miller2022NavajoNation).…”

Impacts of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 variants Alpha and Delta on Coronavirus Disease 2019 transmission dynamics in four metropolitan areas of the United States

“…Simulations and Bayesian inferences were performed as previously described [5–7] and in the Appendix. Files needed to reproduce inferences using the software package PyBioNetFit [28] are available online (https://github.com/lanl/PyBNF/tree/master/examples/Vax_and_Variants). The files include case data, vaccination data, and diagnostic plots related to Bayesian inference using Markov chain Monte Carlo (MCMC) sampling.…”