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Combinatorial complexity and compositional drift in protein interaction networks.
Deeds E. J., Krivine J., Feret J., Danos V., Fontana W.
PLoS ONE 7, 3 (2012) - http://hal.inria.fr/hal-00677889
Articles dans des revues avec comité de lecture
Sciences du Vivant/Bio-Informatique, Biologie Systémique
Combinatorial complexity and compositional drift in protein interaction networks.
Eric J. Deeds ( , http://www.bioinformatics.ku.edu/people/deeds) 1, Jean Krivine (http://www.pps.jussieu.fr/~jkrivine/homepage/Home.html) 2, Jérôme Feret (, http://www.di.ens.fr/~feret) 3, Vincent Danos (, http://homepages.inf.ed.ac.uk/vdanos/home_page.html) 4, Walter Fontana ( , http://fontana.med.harvard.edu/www/index.htm) 5
1 :  Deeds Lab
University of Kansas
Center for Bioinformatics and Department of Molecular Biosciences 2030 Becker Dr. Lawrence, KS 66047
2 :  Preuves, Programmes et Systèmes (PPS)
CNRS : UMR7126 – Université Paris VII - Paris Diderot
Université Paris Diderot, Bât. Sophie Germain, case postale 7014, 75205 Paris Cedex 13
3 :  ABSTRACTION (INRIA Rocquencourt)
INRIA – École normale supérieure [ENS] - Paris – CNRS : UMR 8548
4 :  School of Informatics (Informatics)
University of Edinburgh
School of Informatics Informatics Forum 10 Crichton Street Edinburgh EH8 9AB
5 :  Harvard Medical School (HMS)
Harvard University
25 Shattuck Street Boston, MA 02115
Deeds lab
Fontana lab
The assembly of molecular machines and transient signaling complexes does not typically occur under circumstances in which the appropriate proteins are isolated from all others present in the cell. Rather, assembly must proceed in the context of large-scale protein-protein interaction (PPI) networks that are characterized both by conflict and combinatorial complexity. Conflict refers to the fact that protein interfaces can often bind many different partners in a mutually exclusive way, while combinatorial complexity refers to the explosion in the number of distinct complexes that can be formed by a network of binding possibilities. Using computational models, we explore the consequences of these characteristics for the global dynamics of a PPI network based on highly curated yeast two-hybrid data. The limited molecular context represented in this data-type translates formally into an assumption of independent binding sites for each protein. The challenge of avoiding the explicit enumeration of the astronomically many possibilities for complex formation is met by a rule-based approach to kinetic modeling. Despite imposing global biophysical constraints, we find that initially identical simulations rapidly diverge in the space of molecular possibilities, eventually sampling disjoint sets of large complexes. We refer to this phenomenon as ''compositional drift". Since interaction data in PPI networks lack detailed information about geometric and biological constraints, our study does not represent a quantitative description of cellular dynamics. Rather, our work brings to light a fundamental problem (the control of compositional drift) that must be solved by mechanisms of assembly in the context of large networks. In cases where drift is not (or cannot be) completely controlled by the cell, this phenomenon could constitute a novel source of phenotypic heterogeneity in cell populations.

Publisher Public Library of Science
ISSN 1932-6203 
Public Library of Science