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The Use of Chemostats in Microbial Systems Biology
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Evaluation of Parallel Tempering to Accelerate Bayesian Parameter Estimation in Systems Biology.

Sanjana Gupta1, Liam Hainsworth1, Justin S Hogg1

  • 1Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA.

Proceedings. Euromicro International Conference on Parallel, Distributed, and Network-Based Processing
|September 4, 2018
PubMed
Summary

Parallel tempering (PT) enhances Bayesian parameter estimation in systems biology by accelerating Markov Chain Monte Carlo (MCMC) sampling. This method improves model convergence, especially for complex biological systems where traditional algorithms like Metropolis-Hastings (MH) may fail.

Keywords:
Bayesian parameter estimationParallel temperingRule-based modelingSystems biology

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Statistical Modeling

Background:

  • Biological models require parameter estimation for accurate predictions.
  • High-dimensional and under-constrained models pose challenges for traditional point-estimate methods.
  • Bayesian inference and Markov Chain Monte Carlo (MCMC) are increasingly used but can be slow.

Purpose of the Study:

  • To compare the performance of parallel tempering (PT) against the Metropolis-Hastings (MH) algorithm for Bayesian parameter estimation in biological models.
  • To evaluate the effectiveness of PT in accelerating MCMC sampling and improving convergence, particularly for complex systems.
  • To introduce a new software package, PTEMPEST, for facilitating Bayesian parameter estimation in rule-based biological modeling.

Main Methods:

  • Comparison of parallel tempering (PT) and Metropolis-Hastings (MH) algorithms.
  • Application of methods to six biological models of varying complexity.
  • Development and integration of the PTEMPEST MATLAB package with BioNetGen software.

Main Results:

  • Parallel tempering (PT) demonstrated accelerated convergence and sampling compared to MH for simpler models.
  • For complex models, PT successfully converged while MH became trapped in local minima.
  • The PTEMPEST package provides a practical tool for Bayesian parameter estimation in systems biology.

Conclusions:

  • Parallel tempering is a superior MCMC method for Bayesian parameter estimation in complex biological systems.
  • The PTEMPEST package offers an accessible solution for researchers using rule-based modeling.
  • Accelerated MCMC sampling is crucial for advancing systems biology research.