Parameter Identification for Markov Models of Biochemical Reactions

2011/02/14 by Aleksandr Andreychenko, Linar Mikeev, Andreychenko, Aleksandr +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computational Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Finance #Quantitative Methods (q-bio.QM) #and Science (cs.CE) #cs.CE #q-bio.QM

paper · pdf · doi:10.48550/arxiv.1102.2819

arxiv created 2011/02/14 · arxiv updated 2011/02/15

Abstract

We propose a numerical technique for parameter inference in Markov models of biological processes. Based on time-series data of a process we estimate the kinetic rate constants by maximizing the likelihood of the data. The computation of the likelihood relies on a dynamic abstraction of the discrete state space of the Markov model which successfully mitigates the problem of state space largeness. We compare two variants of our method to state-of-the-art, recently published methods and demonstrate their usefulness and efficiency on several case studies from systems biology.

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