Title :
Bayesian model selection for the yeast GATA-factor network: A comparison of computational approaches
Author :
Milias-Argeitis, Andreas ; Porreca, Riccardo ; Summers, Sean ; Lygeros, John
Author_Institution :
Dept. of Inf. Technol. & Electr. Eng., ETH Zurich, Zurich, Switzerland
Abstract :
A common situation in System Biology is to use several alternative models of a given biochemical system, each with a different structure reflecting different biological hypotheses. These models then have to be ranked according to their ability to reproduce experimental data. In this paper, we use Bayesian model selection to test four alternative models of the yeast GATA-factor genetic network. We employ three different computational methods to calculate the necessary probabilities and evaluate their performance for medium-scale biochemical systems.
Keywords :
Bayes methods; biology computing; Bayesian model selection; alternative models; biological hypothesis; computational approach; computational method; experimental data; medium-scale biochemical systems; system biology; yeast GATA-factor genetic network; yeast GATA-factor network; Approximation algorithms; Bayesian methods; Biological system modeling; Computational modeling; Data models; Mathematical model; Monte Carlo methods;
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-7745-6
DOI :
10.1109/CDC.2010.5717307
