Information propagation within the Genetic Network of Saccharomyces cerevisiae

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http://hdl.handle.net/10138/164359

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Chowdhury , S , Lloyd-Price , J , Smolander , O-P , Baici , W C V , Hughes , T R , Yli-Harja , O , Chua , G & Ribeiro , A S 2010 , ' Information propagation within the Genetic Network of Saccharomyces cerevisiae ' BMC Systems Biology , vol. 4 , 143 . DOI: 10.1186/1752-0509-4-143

Title: Information propagation within the Genetic Network of Saccharomyces cerevisiae
Author: Chowdhury, Sharif; Lloyd-Price, Jason; Smolander, Olli-Pekka; Baici, Wayne C. V.; Hughes, Timothy R.; Yli-Harja, Olli; Chua, Gordon; Ribeiro, Andre S.
Contributor: University of Helsinki, Institute of Biotechnology
Date: 2010-10-26
Language: eng
Number of pages: 10
Belongs to series: BMC Systems Biology
ISSN: 1752-0509
URI: http://hdl.handle.net/10138/164359
Abstract: Background: A gene network's capacity to process information, so as to bind past events to future actions, depends on its structure and logic. From previous and new microarray measurements in Saccharomyces cerevisiae following gene deletions and overexpressions, we identify a core gene regulatory network (GRN) of functional interactions between 328 genes and the transfer functions of each gene. Inferred connections are verified by gene enrichment. Results: We find that this core network has a generalized clustering coefficient that is much higher than chance. The inferred Boolean transfer functions have a mean p-bias of 0.41, and thus similar amounts of activation and repression interactions. However, the distribution of p-biases differs significantly from what is expected by chance that, along with the high mean connectivity, is found to cause the core GRN of S. cerevisiae's to have an overall sensitivity similar to critical Boolean networks. In agreement, we find that the amount of information propagated between nodes in finite time series is much higher in the inferred core GRN of S. cerevisiae than what is expected by chance. Conclusions: We suggest that S. cerevisiae is likely to have evolved a core GRN with enhanced information propagation among its genes.
Subject: REGULATORY NETWORKS
DYNAMICS
EXPRESSION
CELLS
TIME
CRITICALITY
MODELS
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