Stochasticity in gene expression and its regulation by non-coding RNAs

Stochasticity in gene expression and its regulation by non-coding RNAs

One of the fundamental problems in biology is understanding how phenotypic variations arise in individuals. Phenotypic variation is generally attributed to genetic or environmental factors. However, in several important cases, phenotypic variations are observed even among genetically identical cells in homogeneous environments. Recent research indicates that such `non-genetic individuality' can arise due to intrinsic stochasticity in the process of gene expression. Correspondingly there is a need to develop a framework for quantitative modeling of stochastic gene expression and its regulation. Of particular interest is modeling of regulation by non-coding...

Date

February 22, 2012 - 10:00am

Location

Howey L5

One of the fundamental problems in biology is understanding how phenotypic variations arise in individuals. Phenotypic variation is generally attributed to genetic or environmental factors. However, in several important cases, phenotypic variations are observed even among genetically identical cells in homogeneous environments. Recent research indicates that such `non-genetic individuality' can arise due to intrinsic stochasticity in the process of gene expression. Correspondingly there is a need to develop a framework for quantitative modeling of stochastic gene expression and its regulation. Of particular interest is modeling of regulation by non-coding RNAs, which is often a critical component of cellular processes such as development, differentiation and cancer.

In this talk, I will discuss approaches developed by my group that lead to new analytical results for stochastic models of gene expression. In biologically relevant limits, we develop a mapping to queueing theory to derive exact results for general models of stochastic gene expression. Focusing on specific regulatory mechanisms, we propose and analyze a comprehensive model for regulation by non-coding RNAs.  The results obtained provide new insights into the role of non-coding RNAs in fine-tuning the noise in gene expression. I will conclude with a discussion of protocols for inferring gene expression parameters from observations of mRNA and protein distributions.