{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/evaluation-of-parallel-tempering-to","title":"Evaluation of Parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology","arxiv_id":"1801.09831","date":"2018-01-30","proceeding":null,"authors":[],"abstract":"Models of biological systems often have many unknown parameters that must be\ndetermined in order for model behavior to match experimental observations.\nCommonly-used methods for parameter estimation that return point estimates of\nthe best-fit parameters are insufficient when models are high dimensional and\nunder-constrained. As a result, Bayesian methods, which treat model parameters\nas random variables and attempt to estimate their probability distributions\ngiven data, have become popular in systems biology. Bayesian parameter\nestimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample\nmodel parameter distributions, but the slow convergence of MCMC sampling can be\na major bottleneck. One approach to improving performance is parallel tempering\n(PT), a physics-based method that uses swapping between multiple Markov chains\nrun in parallel at different temperatures to accelerate sampling. The\ntemperature of a Markov chain determines the probability of accepting an\nunfavorable move, so swapping with higher temperatures chains enables the\nsampling chain to escape from local minima. In this work we compared the MCMC\nperformance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on\nsix biological models of varying complexity. We found that for simpler models\nPT accelerated convergence and sampling, and that for more complex models, PT\noften converged in cases MH became trapped in non-optimal local minima. We also\ndeveloped a freely-available MATLAB package for Bayesian parameter estimation\ncalled PTempEst (http://github.com/RuleWorld/ptempest), which is closely\nintegrated with the popular BioNetGen software for rule-based modeling of\nbiological systems.","url_abs":"http://arxiv.org/abs/1801.09831v1","url_pdf":"http://arxiv.org/pdf/1801.09831v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"evaluation-of-parallel-tempering-to","repo_url":"https://github.com/RuleWorld/ptempest","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}