An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems

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dc.contributor.author Lu, Chuan
dc.contributor.author King, Ross Donald
dc.date.accessioned 2010-01-14T17:34:03Z
dc.date.available 2010-01-14T17:34:03Z
dc.date.issued 2009-06-17
dc.identifier.citation Lu , C & King , R D 2009 , ' An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems ' Bioinformatics , vol 25 , no. 16 , pp. 2020-2027 . en
dc.identifier.issn 1367-4803
dc.identifier.other PURE: 143343
dc.identifier.other dspace: 2160/3982
dc.identifier.uri http://hdl.handle.net/2160/3982
dc.identifier.uri http://bioinformatics.oxfordjournals.org/cgi/content/abstract/25/16/2020 en
dc.description Lu, C., King, R. D. (2009). An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems. Bioinformatics, 25 (16), 2020-2027 Sponsorship: BBSRC MeTRO project and the EU project UNICELLSYS (FP7-Health-201142) en
dc.description.abstract Motivation: Distribution analysis is one of the most basic forms of statistical analysis. Thanks to improved analytical methods, accurate and extensive quantitative measurements can now be made of the mRNA, protein and metabolite from biological systems. Here, we report a large-scale analysis of the population abundance distributions of the transcriptomes, proteomes and metabolomes from varied biological systems. Results: We compared the observed empirical distributions with a number of distributions: power law, lognormal, loglogistic, loggamma, right Pareto-lognormal (PLN) and double PLN (dPLN). The best-fit for mRNA, protein and metabolite population abundance distributions was found to be the dPLN. This distribution behaves like a lognormal distribution around the centre, and like a power law distribution in the tails. To better understand the cause of this observed distribution, we explored a simple stochastic model based on geometric Brownian motion. The distribution indicates that multiplicative effects are causally dominant in biological systems. We speculate that these effects arise from chemical reactions: the central-limit theorem then explains the central lognormal, and a number of possible mechanisms could explain the long tails: positive feedback, network topology, etc. Many of the components in the central lognormal parts of the empirical distributions are unidentified and/or have unknown function. This indicates that much more biology awaits discovery. en
dc.format.extent 8 en
dc.language.iso eng
dc.relation.ispartof Bioinformatics en
dc.title An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1093/bioinformatics/btp360
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.description.status Peer reviewed en


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