An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems.
BACKGROUND
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.
Lu
Chuan
C
Department of Computer Science, Aberystwyth University, Ceredigion SY23 3DB, UK.
King
Ross D
RD
D003198:Computer Simulation; D055442:Metabolome; D008954:Models, Biological; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012333:RNA, Messenger / Q000378:metabolism*
D011506:Proteins; D012333:RNA, Messenger