Stochastic Simulations of the Origins and Implications of Long-Tailed Distributions in Gene Expression

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 102; no. 13; pp. 4771 - 4776
• Main Authors: Krishna, Sandeep, Banerjee, Bidisha, Ramakrishnan, T. V., Shivashankar, G. V., Cantor, Charles R.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 29.03.2005; National Acad Sciences
• Subjects: Animals; Biological Sciences; Cell cycle; Cell division; Computer Simulation; Crossovers; DNA; DNA-Binding Proteins - metabolism; Drosophila; Drosophila Proteins - metabolism; Embryo, Nonmammalian - metabolism; Embryos; Fluorescence; Gaussian distributions; Gene Expression; Gene Expression Regulation; Homeodomain Proteins - metabolism; Modeling; Models, Chemical; Operator Regions, Genetic - genetics; Promoter Regions, Genetic - genetics; Proteins; Proteins - metabolism; Simulation; Standard deviation; Stochastic models; Stochastic Processes; Trans-Activators - metabolism; Transcription Factors - metabolism
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.0406415102

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.