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

TL;DR

This paper introduces a practical MCMC sampling algorithm integrated into PyBioNetFit, enabling efficient Bayesian inference for biological models, including real-world applications like COVID-19 forecasting.

Contribution

The implementation of an adaptive MCMC method in PyBioNetFit supports parallel chains and warm starts, improving Bayesian parameter estimation in biological systems modeling.

Findings

Successfully applied to COVID-19 case forecasting

Supports parallel chains on clusters

Provides adaptive proposal distribution for better sampling

Abstract

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. 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.