Combinatorial Complexity and Compositional Drift in Protein Interaction Networks
dc.contributor.author | Deeds, Eric J. | |
dc.contributor.author | Krivine, Jean | |
dc.contributor.author | Feret, Jérôme | |
dc.contributor.author | Danos, Vincent | |
dc.contributor.author | Fontana, Walter | |
dc.date.accessioned | 2014-03-18T16:20:55Z | |
dc.date.available | 2014-03-18T16:20:55Z | |
dc.date.issued | 2012-03-08 | |
dc.identifier.citation | Deeds, E. J., Krivine, J., Feret, J., Danos, V., & Fontana, W. (2012). Combinatorial Complexity and Compositional Drift in Protein Interaction Networks. PLoS ONE, 7(3). http://dx.doi.org/10.1371/journal.pone.0032032 | |
dc.identifier.uri | http://hdl.handle.net/1808/13225 | |
dc.description.abstract | 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. | |
dc.description.sponsorship | This research has been supported by National Research Service Award F32 GM080123-03 to EJD. | |
dc.publisher | Public Library of Science | |
dc.rights | ©2012 Deeds et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Biochemical simulation | |
dc.subject | Biophysical simulation | |
dc.subject | Chemical dissociation | |
dc.subject | Free energy | |
dc.subject | Graphs | |
dc.subject | Protein interaction networks | |
dc.subject | Protein interactions | |
dc.subject | Simulation and modeling | |
dc.title | Combinatorial Complexity and Compositional Drift in Protein Interaction Networks | |
dc.type | Article | |
kusw.kuauthor | Deeds, Eric J. | |
kusw.kudepartment | Molecular Biosciences | |
kusw.oastatus | fullparticipation | |
dc.identifier.doi | 10.1371/journal.pone.0032032 | |
kusw.oaversion | Scholarly/refereed, publisher version | |
kusw.oapolicy | This item meets KU Open Access policy criteria. | |
dc.rights.accessrights | openAccess |
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Except where otherwise noted, this item's license is described as: ©2012 Deeds et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.