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

Andreas Milias-Argeitis; Riccardo Porreca; Sean Summers; John Lygeros

doi:10.1109/CDC.2010.5717307

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

Andreas Milias-Argeitis^*, Riccardo Porreca, Sean Summers, John Lygeros

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

7 Citations (Scopus)

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.

Original language	English
Title of host publication	2010 49th IEEE Conference on Decision and Control, CDC 2010
Pages	3379-3384
Number of pages	6
DOIs	https://doi.org/10.1109/CDC.2010.5717307
Publication status	Published - 2010
Externally published	Yes
Event	49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA, United States Duration: 15-Dec-2010 → 17-Dec-2010

Conference

Conference	49th IEEE Conference on Decision and Control, CDC 2010
Country/Territory	United States
City	Atlanta, GA
Period	15/12/2010 → 17/12/2010

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@inproceedings{2c047316d8224680b726f230ba59a691,

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

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.",

author = "Andreas Milias-Argeitis and Riccardo Porreca and Sean Summers and John Lygeros",

year = "2010",

doi = "10.1109/CDC.2010.5717307",

language = "English",

isbn = "9781424477456",

pages = "3379--3384",

booktitle = "2010 49th IEEE Conference on Decision and Control, CDC 2010",

note = "49th IEEE Conference on Decision and Control, CDC 2010 ; Conference date: 15-12-2010 Through 17-12-2010",

}

Milias-Argeitis, A, Porreca, R, Summers, S & Lygeros, J 2010, Bayesian model selection for the yeast GATA-factor network: A comparison of computational approaches. in 2010 49th IEEE Conference on Decision and Control, CDC 2010., 5717307, pp. 3379-3384, 49th IEEE Conference on Decision and Control, CDC 2010, Atlanta, GA, United States, 15/12/2010. https://doi.org/10.1109/CDC.2010.5717307

Bayesian model selection for the yeast GATA-factor network: A comparison of computational approaches. / Milias-Argeitis, Andreas; Porreca, Riccardo; Summers, Sean et al.
2010 49th IEEE Conference on Decision and Control, CDC 2010. 2010. p. 3379-3384 5717307.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Porreca, Riccardo

AU - Summers, Sean

AU - Lygeros, John

PY - 2010

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N2 - 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.

AB - 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.

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DO - 10.1109/CDC.2010.5717307

M3 - Conference contribution

AN - SCOPUS:79953131780

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BT - 2010 49th IEEE Conference on Decision and Control, CDC 2010

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ER -

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

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