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

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

doi:10.1093/bioinformatics/btac004

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

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

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

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.

Original language	English (US)
Pages (from-to)	1770-1772
Number of pages	3
Journal	Bioinformatics
Volume	38
Issue number	6
DOIs	https://doi.org/10.1093/bioinformatics/btac004
State	Published - Mar 15 2022

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btac004

Cite this

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title = "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.",

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AU - Neumann, Jacob

AU - Lin, Yen Ting

AU - Mallela, Abhishek

AU - Miller, Ely F.

AU - Colvin, Joshua

AU - Duprat, Abell T.

AU - Chen, Ye

AU - Hlavacek, William S.

AU - Posner, Richard G.

PY - 2022/3/15

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AB - 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.

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

Abstract

ASJC Scopus subject areas

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