Stochastic modeling of regulation of gene expression by multiple small RNAs

A wealth of new research has highlighted the critical roles of small RNAs (sRNAs) in diverse processes such as quorum sensing and cellular responses to stress. The pathways controlling these processes often have a central motif comprising of a master regulator protein whose expression is controlled by multiple sRNAs. However, the regulation of stochastic gene expression of a single target gene by multiple sRNAs is currently not well understood. To address this issue, we analyze a stochastic model of regulation of gene expression by multiple sRNAs. For this model, we derive exact analytic results for the regulated protein distribution including compact expressions for its mean and variance. The derived results provide novel insights into the roles of multiple sRNAs in fine-tuning the noise in gene expression. In particular, we show that, in contrast to regulation by a single sRNA, multiple sRNAs provide a mechanism for independently controlling the mean and variance of the regulated protein distribution

Regulation by small RNAs via coupled degradation: mean-field and variational approaches

Regulatory genes called small RNAs (sRNAs) are known to play critical roles in cellular responses to changing environments. For several sRNAs, regulation is effected by coupled stoichiometric degradation with messenger RNAs (mRNAs). The nonlinearity inherent in this regulatory scheme indicates that exact analytical solutions for the corresponding stochastic models are intractable. Here, we present a variational approach to analyze a well-studied stochastic model for regulation by sRNAs via coupled degradation. The proposed approach is efficient and provides accurate estimates of mean mRNA levels as well as higher order terms. Results from the variational ansatz are in excellent agreement with data from stochastic simulations for a wide range of parameters, including regions of parameter space where mean-field approaches break down. The proposed approach can be applied to quantitatively model stochastic gene expression in complex regulatory networks.Comment: 4 pages, 3 figure

Exact protein distributions for stochastic models of gene expression using partitioning of Poisson processes

Stochasticity in gene expression gives rise to fluctuations in protein levels across a population of genetically identical cells. Such fluctuations can lead to phenotypic variation in clonal populations, hence there is considerable interest in quantifying noise in gene expression using stochastic models. However, obtaining exact analytical results for protein distributions has been an intractable task for all but the simplest models. Here, we invoke the partitioning property of Poisson processes to develop a mapping that significantly simplifies the analysis of stochastic models of gene expression. The mapping leads to exact protein distributions using results for mRNA distributions in models with promoter-based regulation. Using this approach, we derive exact analytical results for steady-state and time-dependent distributions for the basic 2-stage model of gene expression. Furthermore, we show how the mapping leads to exact protein distributions for extensions of the basic model that include the effects of post-transcriptional and post-translational regulation. The approach developed in this work is widely applicable and can contribute to a quantitative understanding of stochasticity in gene expression and its regulation.Comment: 10 pages, 5 figure

Prediction of CsrA-regulating small RNAs in bacteria and their experimental verification in Vibrio fischeri

The role of small RNAs as critical components of global regulatory networks has been highlighted by several recent studies. An important class of such small RNAs is represented by CsrB and CsrC of Escherichia coli, which control the activity of the global regulator CsrA. Given the critical role played by CsrA in several bacterial species, an important problem is the identification of CsrA-regulating small RNAs. In this paper, we develop a computer program (CSRNA_FIND) designed to locate potential CsrA-regulating small RNAs in bacteria. Using CSRNA_FIND to search the genomes of bacteria having homologs of CsrA, we identify all the experimentally known CsrA-regulating small RNAs and also make predictions for several novel small RNAs. We have verified experimentally our predictions for two CsrA-regulating small RNAs in Vibrio fischeri. As more genomes are sequenced, CSRNA_FIND can be used to locate the corresponding small RNAs that regulate CsrA homologs. This work thus opens up several avenues of research in understanding the mode of CsrA regulation through small RNAs in bacteria