Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks
Metadata only
Datum
2015-04Typ
- Journal Article
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. Mehr anzeigen
Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
ACM Transactions on Modeling and Computer SimulationBand
Seiten / Artikelnummer
Verlag
Association for Computing MachineryThema
Continuous-time Markov chains; Experiment design; Fisher information; Moment equations; Parameter inferenceOrganisationseinheit
03751 - Lygeros, John / Lygeros, John