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An in silico target identification using Boolean network attractors: Avoiding pathological phenotypes.
MedLine Citation:
PMID:  25433558     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Target identification aims at identifying biomolecules whose function should be therapeutically altered to cure the considered pathology. An algorithm for in silico target identification using Boolean network attractors is proposed. It assumes that attractors correspond to phenotypes produced by the modeled biological network. It identifies target combinations which allow disturbed networks to avoid attractors associated with pathological phenotypes. The algorithm is tested on a Boolean model of the mammalian cell cycle and its applications are illustrated on a Boolean model of Fanconi anemia. Results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice. Nevertheless, it is expected that the algorithm is of interest for target identification.
Authors:
Arnaud Poret; Jean-Pierre Boissel
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Publication Detail:
Type:  Journal Article     Date:  2014-11-11
Journal Detail:
Title:  Comptes rendus biologies     Volume:  337     ISSN:  1768-3238     ISO Abbreviation:  C. R. Biol.     Publication Date:  2014 Dec 
Date Detail:
Created Date:  2014-12-01     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101140040     Medline TA:  C R Biol     Country:  France    
Other Details:
Languages:  eng     Pagination:  661-78     Citation Subset:  IM    
Copyright Information:
Copyright © 2014 Académie des sciences. Published by Elsevier SAS. All rights reserved.
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