Document Detail


Evolving sensitivity balances Boolean Networks.
MedLine Citation:
PMID:  22586459     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
We investigate the sensitivity of Boolean Networks (BNs) to mutations. We are interested in Boolean Networks as a model of Gene Regulatory Networks (GRNs). We adopt Ribeiro and Kauffman's Ergodic Set and use it to study the long term dynamics of a BN. We define the sensitivity of a BN to be the mean change in its Ergodic Set structure under all possible loss of interaction mutations. In silico experiments were used to selectively evolve BNs for sensitivity to losing interactions. We find that maximum sensitivity was often achievable and resulted in the BNs becoming topologically balanced, i.e. they evolve towards network structures in which they have a similar number of inhibitory and excitatory interactions. In terms of the dynamics, the dominant sensitivity strategy that evolved was to build BNs with Ergodic Sets dominated by a single long limit cycle which is easily destabilised by mutations. We discuss the relevance of our findings in the context of Stem Cell Differentiation and propose a relationship between pluripotent stem cells and our evolved sensitive networks.
Authors:
Jamie X Luo; Matthew S Turner
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-05-07
Journal Detail:
Title:  PloS one     Volume:  7     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2012  
Date Detail:
Created Date:  2012-05-15     Completed Date:  2012-09-26     Revised Date:  2013-06-25    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e36010     Citation Subset:  IM    
Affiliation:
Centre for Complexity Science, University of Warwick, Coventry, West Midlands, United Kingdom.
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MeSH Terms
Descriptor/Qualifier:
Cell Differentiation / genetics
Computer Simulation
Gene Expression*
Gene Regulatory Networks*
Models, Theoretical*
Mutation
Stem Cells
Comments/Corrections

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