Stochastic filters based on hybrid approximations of multiscale stochastic reaction networks
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
We consider the problem of estimating the dynamic latent states of an intracellular multiscale stochastic reaction network from time-course measurements of fluorescent reporters. We first prove that accurate solutions to the filtering problem can be constructed by solving the filtering problem for a reduced model that represents the dynamics as a hybrid process. The model reduction is based on exploiting the time-scale separations in the original network, and it can greatly reduce the computational effort required to simulate the dynamics. This enables us to develop efficient particle filters to solve the filtering problem for the original model by applying particle filters to the reduced model. We illustrate the accuracy and the computational efficiency of our approach using a numerical example.
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published
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2020 59th IEEE Conference on Decision and Control (CDC)
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4616 - 4621
Publisher
IEEE
Event
59th IEEE Conference on Decision and Control (CDC 2020) (virtual)
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03921 - Khammash, Mustafa / Khammash, Mustafa
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Funding
182653 - An Advanced Stochastic Filtering Framework for the Analysis of Multiscale Biochemical Reaction Networks (SNF)