Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks

0444817 - MÚ 2016 RIV GB eng J - Journal Article
Liao, S. - Vejchodský, Tomáš - Erban, R.
Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks.
Journal of the Royal Society Interface. Roč. 12, č. 108 (2015), s. 20150233. ISSN 1742-5689. E-ISSN 1742-5662
EU Projects: European Commission(XE) 328008 - STOCHDETBIOMODEL
Institutional support: RVO:67985840
Keywords : gene regulatory networks * stochastic modelling * parametric analysis
Subject RIV: BA - General Mathematics
Impact factor: 3.818, year: 2015
http://rsif.royalsocietypublishing.org/content/12/108/20150233

In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.
Permanent Link: http://hdl.handle.net/11104/0247287