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. © 2011 Springer-Verlag.
CITATION STYLE
Andreychenko, A., Mikeev, L., Spieler, D., & Wolf, V. (2011). Parameter identification for Markov models of biochemical reactions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6806 LNCS, pp. 83–98). https://doi.org/10.1007/978-3-642-22110-1_8
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