Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks Journal Article


Author(s): Ruess, Jakob; Lygeros, John
Article Title: Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks
Affiliation IST Austria
Abstract: Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular 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.
Keywords: Continuous-timeMarkov chains; Experiment design; Fisher information; Moment equations; Parameter inference
Journal Title: ACM Transactions on Modeling and Computer Simulation
Volume: 25
Issue 2
ISSN: 1049-3301
Publisher: ACM  
Date Published: 2015-02-01
Start Page: Article number: 8
Sponsor: HYCON2; EC; European Commission
DOI: 10.1145/2688906
Notes: This article summarizes the theory behind the methods that were originally proposed in Zechner et al. [2012] and Ruess et al. [2013]. We are grateful to the coauthors of these papers for their contributions to the work.
Open access: no