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

@article{citeulike:4920070, 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 metabolites species 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, and double Pareto-lognormal. The best-fit for mRNA, protein, and metabolite population abundance distributions was found to be the double Pareto-lognormal. 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 - positivefeedback effects, 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. CONTACT: rdk@aber.ac.uk.}, author = {Lu, Chuan and King, Ross D.}, citeulike-article-id = {4920070}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btp360}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19535531}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19535531}, day = {17}, doi = {10.1093/bioinformatics/btp360}, issn = {1460-2059}, journal = {Bioinformatics (Oxford, England)}, keywords = {metabolism, mrna, statistical-model, statistics, systems-biology}, month = {June}, posted-at = {2009-06-21 15:21:52}, priority = {2}, title = {An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems.}, url = {http://dx.doi.org/10.1093/bioinformatics/btp360}, year = {2009} }

TY - JOUR ID - citeulike:4920070 L3 - citeulike-article-id:4920070 N2 - 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 metabolites species 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, and double Pareto-lognormal. The best-fit for mRNA, protein, and metabolite population abundance distributions was found to be the double Pareto-lognormal. 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 - positivefeedback effects, 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. CONTACT: rdk@aber.ac.uk. JF - Bioinformatics (Oxford, England) SN - 1460-2059 TI - An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems. KW - metabolism KW - mrna KW - statistical-model KW - statistics KW - systems-biology AU - Lu, Chuan AU - King, Ross PY - 2009/06/17/ UR - http://dx.doi.org/10.1093/bioinformatics/btp360 ER -