Convergence of Regularized Particle Filters for Stochastic Reaction Networks
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Date
2023
Publication Type
Journal Article
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Abstract
Filtering for stochastic reaction networks (SRNs) is an important problem in sys-tems/synthetic biology aiming to estimate the state of unobserved chemical species. A good solution to it can provide scientists valuable information about the hidden dynamic state and enable opti-mal feedback control. Usually, the model parameters need to be inferred simultaneously with state variables, and a conventional particle filter can fail to solve this problem accurately due to sample degeneracy. In this case, the regularized particle filter (RPF) is preferred to the conventional ones, as the RPF can mitigate sample degeneracy by perturbing particles with artificial noise. However, the artificial noise introduces an additional bias to the estimate, and, thus, it is questionable whether the RPF can provide reliable results for SRNs. In this paper, we aim to identify conditions under which the RPF converges to the exact filter in the filtering problem determined by a bimolecular net-work. First, we establish computationally efficient RPFs for SRNs on different scales using different dynamical models, including the continuous-time Markov process, tau-leaping model, and piecewise deterministic process. Then, by parameter sensitivity analyses, we show that the established RPFs converge to the exact filters if all reactions leading to an increase of the molecular population have linearly growing propensities and some other mild conditions are satisfied simultaneously. This en-sures the performance of the RPF for a large class of SRNs, and several numerical examples are presented to illustrate our results.
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published
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Volume
61 (2)
Pages / Article No.
399 - 430
Publisher
SIAM
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Subject
regularized particle filters; stochastic reaction networks; multiscale systems; filtering theory
Organisational unit
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)