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      State and parameter estimation from exact partial state observation in stochastic reaction networks

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          Abstract

          We consider chemical reaction networks modeled by a discrete state and continuous in time Markov process for the vector copy number of the species and provide a novel particle filter method for state and parameter estimation based on exact observation of some of the species in continuous time. The conditional probability distribution of the unobserved states is shown to satisfy a system of differential equations with jumps. We provide a method of simulating a process that is a proxy for the vector copy number of the unobserved species along with a weight. The resulting weighted Monte Carlo simulation is then used to compute the conditional probability distribution of the unobserved species. Our method is exact in a certain sense and we also show how our algorithm can be adapted for a Bayesian estimation of parameters and for the estimation of a past state value based on observations up to a future time.

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          Author and article information

          Journal
          08 October 2020
          Article
          2010.04346
          3b869df4-a039-4e96-b882-3e6b3f8322a3

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          q-bio.MN math.PR

          Molecular biology,Probability
          Molecular biology, Probability

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