Parameter Identification for Markov Models of Biochemical Reactions

  title={Parameter Identification for Markov Models of Biochemical Reactions},
  author={Aleksandr Andreychenko and Linar Mikeev and David Spieler and Verena Wolf},
We propose a numerical technique for parameter inference in Markov models of biological processes. Based on time-series data of a proc ess we estimate the kinetic rate constants by maximizing the likelihood of the d ata. The computation of the likelihood relies on a dynamic abstraction of the disc rete state space of the Markov model which successfully mitigates the problem of st ate space largeness. We compare two variants of our method to state-of-the-art, r ecently published methods and… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 22 references

Maximum likelihood estimation of the kinetics of receptor-mediated adhesion.

Journal of theoretical biology • 2010
View 4 Excerpts
Highly Influenced

Fas t daptive uniformisation of the chemical master equation

M. Mateescu, V. Wolf, F. Didier, T. A. Henzinger
IET Systems Biology • 2010
View 1 Excerpt

Approximate Bayesian computation scheme for parameter inference and model selec tion in dynamical systems.J

T. Toni, D. Welch, N. Strelkowa, A. Ipsen, M. Stumpf
R. Soc. Interface • 2009

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