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

Title:
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit
Authors:
Jacob Neumann, Yen Ting Lin, Abhishek Mallela, Ely F Miller, Joshua Colvin, Abell T Duprat, Ye Chen, William S Hlavacek, Richard G Posner
Source:
Bioinformatics. 38:1770-1772
Publication Status:
Preprint
Publisher Information:
Oxford University Press (OUP), 2022.
Publication Year:
2022
Subject Terms:
FOS: Biological sciences, Humans, COVID-19, Bayes Theorem, Quantitative Biology - Quantitative Methods, Monte Carlo Method, Markov Chains, Algorithms, Software, Quantitative Methods (q-bio.QM), 3. Good health
Document Type:
Fachzeitschrift Article
Language:
English
ISSN:
1367-4811
1367-4803
DOI:
10.1093/bioinformatics/btac004
DOI:
10.48550/arxiv.2109.14445
Access URL:
http://arxiv.org/pdf/2109.14445
https://pubmed.ncbi.nlm.nih.gov/34986226
http://arxiv.org/abs/2109.14445
Rights:
OUP Standard Publication Reuse
CC BY NC ND
Accession Number:
edsair.doi.dedup.....68c8fb0ff8ff276d1a751aa95cfa2eea
Database:
OpenAIRE

Weitere Informationen

SummaryBayesian 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 implementationPyBNF 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 informationSupplementary data are available at Bioinformatics online.