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

J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation 25 (2015).

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Journal Article | Published | English
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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.
Publishing Year
Date Published
2015-02-01
Journal Title
ACM Transactions on Modeling and Computer Simulation
Acknowledgement
HYCON2; EC; European Commission
Volume
25
Issue
2
Article Number
8
IST-REx-ID

Cite this

Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 2015;25(2). doi:10.1145/2688906
Ruess, J., & Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation, 25(2). https://doi.org/10.1145/2688906
Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions on Modeling and Computer Simulation 25, no. 2 (2015). https://doi.org/10.1145/2688906.
J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2, 2015.
Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 25(2).
Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2, 8, ACM, 2015, doi:10.1145/2688906.

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