Surrogate assisted model reduction for stochastic biochemical reaction networks | IEEE Conference Publication | IEEE Xplore

Surrogate assisted model reduction for stochastic biochemical reaction networks


Abstract:

Cellular regulatory mechanisms are typically governed by biochemical reaction networks. Discrete stochastic models are widely used in computational systems biology to ana...Show More

Abstract:

Cellular regulatory mechanisms are typically governed by biochemical reaction networks. Discrete stochastic models are widely used in computational systems biology to analyze such networks. Often, the models involve a large number of highly uncertain parameters and many interacting chemical species. However, one is often interested in observing the output of one, or a few of the species rather than the entire network. Simulating the complete reaction network is inefficient in such cases. This paper explores the use of surrogate models to learn partial stochastic biochemical reaction networks and enable fast near-instant evaluation. The efficacy of the proposed method is demonstrated on a model from the systems biology literature.
Date of Conference: 03-06 December 2017
Date Added to IEEE Xplore: 08 January 2018
ISBN Information:
Electronic ISSN: 1558-4305
Conference Location: Las Vegas, NV, USA

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