research-article

Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks

Authors:

John LygerosAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 25, Issue 2

Article No.: 8, Pages 1 - 25

https://doi.org/10.1145/2688906

Published: 17 February 2015 Publication History

Abstract

Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of the molecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance.

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Index Terms

Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks
1. Mathematics of computing
  1. Probability and statistics

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Published In

cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 25, Issue 2

Special Issue on Computational Methods in Systems Biology

April 2015

161 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/2737798

Editor:
Adelinde M. Uhrmacher
University of Rostock, Germany

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Copyright © 2015 ACM.

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Publication History

Published: 17 February 2015

Accepted: 01 July 2014

Revised: 01 June 2014

Received: 01 January 2014

Published in TOMACS Volume 25, Issue 2

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Pietzsch TDuso LZechner C(2021)Compartor: a toolbox for the automatic generation of moment equations for dynamic compartment populationsBioinformatics10.1093/bioinformatics/btab058Online publication date: 4-Feb-2021
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Warne DBaker RSimpson M(2021)Rapid Bayesian Inference for Expensive Stochastic ModelsJournal of Computational and Graphical Statistics10.1080/10618600.2021.200041931:2(512-528)Online publication date: 16-Dec-2021
https://doi.org/10.1080/10618600.2021.2000419
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