  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.
