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Bayesian model selection for the yeast GATA-factor network: A comparison of computational approaches

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Conference object 01 Dec 2010Embargo end date: 17 Dec 2010 Switzerland Publisher:IEEEJournal:49th IEEE Conference on Decision and Control (CDC)

Authors: Milias-Argeitis, Andreas; Porreca, Riccardo; Summers, Sean; Lygeros, John;

doi: 10.1109/cdc.2010.5717307 , 10.5167/uzh-79163

Bayesian model selection for the yeast GATA-factor network: A comparison of computational approaches

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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.

Country

Switzerland

Related Organizations

University of Zurich
Switzerland
ETH Zurich
Switzerland
Automatic Control Laboratory
Switzerland

Keywords

2606 Control and Optimization, SX00 SystemsX.ch, 570 Life sciences; biology, SX16 YeastX, 2207 Control and Systems Engineering, 2611 Modeling and Simulation

22 Research products, page 1 of 3

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