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Finding the "Dark Matter'' in Human and Yeast Protein Network Prediction and Modelling

Ranea, JAG; Morilla, I; Lees, JG; Reid, AJ; Yeats, C; Clegg, AB; Sanchez-Jimenez, F; (2010) Finding the "Dark Matter'' in Human and Yeast Protein Network Prediction and Modelling. PLOS COMPUT BIOL , 6 (9) , Article e1000945. 10.1371/journal.pcbi.1000945. Green open access

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Abstract

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or "dark matter'' of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions.

Type: Article
Title: Finding the "Dark Matter'' in Human and Yeast Protein Network Prediction and Modelling
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pcbi.1000945
Publisher version: http://dx.doi.org/10.1371/journal.pcbi.1000945
Language: English
Additional information: © 2010 Ranea 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. Funding: Juan A. G. Ranea acknowledges funding from the Spanish Science and Innovation Ministry (http://web.micinn.es/) through the Ramon y Cajal program (RYC-2007-01649) and the Plan Nacional project (SAF2009-09839, Subprograma MED), and also the EU-funded ENFIN project. Ian Morilla acknowledges funding from the Andalusian Government BIO-267; Jon G. Lees from the Wellcome Trust and the ENFIN Network of Excellence; Adam J. Reid from the BBSRC; Corin Yeats from the Biosapiens European Network of Excellence; Andrew B. Clegg from the EMBRACE European Network of Excellence; and Francisca Sanchez-Jimenez acknowledges CIBER de Enfermedades Raras, an initiative of the ISCIII and the Spanish Plan Nacional project SAF2008-02522. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Keywords: MONTE-CARLO-METHOD, INTERACTION DATABASE, FUNCTIONAL LINKAGES, COMPLEX NETWORKS, DATA INTEGRATION, GENE ONTOLOGY, RESOURCE, CLASSIFICATION, COEVOLUTION, ANNOTATION
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology
URI: https://discovery.ucl.ac.uk/id/eprint/168833
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