Finding the "dark matter" in human and yeast protein network prediction and modelling.

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...

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Autores principales:	Juan A G Ranea, Ian Morilla, Jon G Lees, Adam J Reid, Corin Yeats, Andrew B Clegg, Francisca Sanchez-Jimenez, Christine Orengo
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/813a90230727425789f66e5449ad6241
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spelling	oai:doaj.org-article:813a90230727425789f66e5449ad62412021-11-18T05:49:17ZFinding the "dark matter" in human and yeast protein network prediction and modelling.1553-734X1553-735810.1371/journal.pcbi.1000945https://doaj.org/article/813a90230727425789f66e5449ad62412010-09-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20885791/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Accurate 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.Juan A G RaneaIan MorillaJon G LeesAdam J ReidCorin YeatsAndrew B CleggFrancisca Sanchez-JimenezChristine OrengoPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 9 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Juan A G Ranea Ian Morilla Jon G Lees Adam J Reid Corin Yeats Andrew B Clegg Francisca Sanchez-Jimenez Christine Orengo Finding the "dark matter" in human and yeast protein network prediction and modelling.
description	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.
format	article
author	Juan A G Ranea Ian Morilla Jon G Lees Adam J Reid Corin Yeats Andrew B Clegg Francisca Sanchez-Jimenez Christine Orengo
author_facet	Juan A G Ranea Ian Morilla Jon G Lees Adam J Reid Corin Yeats Andrew B Clegg Francisca Sanchez-Jimenez Christine Orengo
author_sort	Juan A G Ranea
title	Finding the "dark matter" in human and yeast protein network prediction and modelling.
title_short	Finding the "dark matter" in human and yeast protein network prediction and modelling.
title_full	Finding the "dark matter" in human and yeast protein network prediction and modelling.
title_fullStr	Finding the "dark matter" in human and yeast protein network prediction and modelling.
title_full_unstemmed	Finding the "dark matter" in human and yeast protein network prediction and modelling.
title_sort	finding the "dark matter" in human and yeast protein network prediction and modelling.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/813a90230727425789f66e5449ad6241
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Finding the "dark matter" in human and yeast protein network prediction and modelling.

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