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Parameter Identification for Markov Models of Biochemical Reactions

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6806)

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.

Keywords

  • State Space
  • Reaction Network
  • Observation Interval
  • Observation Sequence
  • Chemical Master Equation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Andreychenko, A., Mikeev, L., Spieler, D., Wolf, V. (2011). Parameter Identification for Markov Models of Biochemical Reactions. In: Gopalakrishnan, G., Qadeer, S. (eds) Computer Aided Verification. CAV 2011. Lecture Notes in Computer Science, vol 6806. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22110-1_8

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  • DOI: https://doi.org/10.1007/978-3-642-22110-1_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22109-5

  • Online ISBN: 978-3-642-22110-1

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