Random Phenotypic Variation of Yeast Saccharomyces cerevisiae Single-Gene Knockouts Fits a Double Pareto-Lognormal DistributionReport as inadecuate




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Background

Distributed robustness is thought to influence the buffering of random phenotypic variation through the scale-free topology of gene regulatory, metabolic, and protein-protein interaction networks. If this hypothesis is true, then the phenotypic response to the perturbation of particular nodes in such a network should be proportional to the number of links those nodes make with neighboring nodes. This suggests a probability distribution approximating an inverse power-law of random phenotypic variation. Zero phenotypic variation, however, is impossible, because random molecular and cellular processes are essential to normal development. Consequently, a more realistic distribution should have a y-intercept close to zero in the lower tail, a mode greater than zero, and a long fat upper tail. The double Pareto-lognormal DPLN distribution is an ideal candidate distribution. It consists of a mixture of a lognormal body and upper and lower power-law tails.

Objective and Methods

If our assumptions are true, the DPLN distribution should provide a better fit to random phenotypic variation in a large series of single-gene knockout lines than other skewed or symmetrical distributions. We fit a large published data set of single-gene knockout lines in Saccharomyces cerevisiae to seven different probability distributions: DPLN, right Pareto-lognormal RPLN, left Pareto-lognormal LPLN, normal, lognormal, exponential, and Pareto. The best model was judged by the Akaike Information Criterion AIC.

Results

Phenotypic variation among gene knockouts in S. cerevisiae fits a double Pareto-lognormal DPLN distribution better than any of the alternative distributions, including the right Pareto-lognormal and lognormal distributions.

Conclusions and Significance

A DPLN distribution is consistent with the hypothesis that developmental stability is mediated, in part, by distributed robustness, the resilience of gene regulatory, metabolic, and protein-protein interaction networks. Alternatively, multiplicative cell growth, and the mixing of lognormal distributions having different variances, may generate a DPLN distribution.



Author: John H. Graham , Daniel T. Robb, Amy R. Poe

Source: http://plos.srce.hr/



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