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Stochastic simulations of the origins and implications of long-tailed distributions in gene expression.

ABSTRACT: Gene expression noise results in protein number distributions ranging from long-tailed to Gaussian. We show how long-tailed distributions arise from a stochastic model of the constituent chemical reactions and suggest that, in conjunction with cooperative switches, they lead to more sensitive selection of a subpopulation of cells with high protein number than is possible with Gaussian distributions. Single-cell-tracking experiments are presented to validate some of the assumptions of the stochastic simulations. We also examine the effect of DNA looping on the shape of protein distributions. We further show that when switches are incorporated in the regulation of a gene via a feedback loop, the distributions can become bimodal. This might explain the bimodal distribution of certain morphogens during early embryogenesis.

SUBMITTER: Krishna S

PROVIDER: S-EPMC555697 | biostudies-literature | 2005 Mar

REPOSITORIES: biostudies-literature

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Stochastic simulations of the origins and implications of long-tailed distributions in gene expression.

Krishna Sandeep S Banerjee Bidisha B Ramakrishnan T V TV Shivashankar G V GV

Proceedings of the National Academy of Sciences of the United States of America 20050316 13

Gene expression noise results in protein number distributions ranging from long-tailed to Gaussian. We show how long-tailed distributions arise from a stochastic model of the constituent chemical reactions and suggest that, in conjunction with cooperative switches, they lead to more sensitive selection of a subpopulation of cells with high protein number than is possible with Gaussian distributions. Single-cell-tracking experiments are presented to validate some of the assumptions of the stochas ...[more]

PMID: 15772163

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