Parameter Identification for Markov Models of Biochemical Reactions
Quantitative Methods
2011-02-15 v1 Computational Engineering, Finance, and Science
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
Cite
@article{arxiv.1102.2819,
title = {Parameter Identification for Markov Models of Biochemical Reactions},
author = {Aleksandr Andreychenko and Linar Mikeev and David Spieler and Verena Wolf},
journal= {arXiv preprint arXiv:1102.2819},
year = {2011}
}