Seminar Details
2015-03-10 (14.00) : Modelling stochastic gene expression using the Master Equation
At Euler building (A002)
Organized by Mathematical Engineering
Speaker :
Martin Hemberg (Wellcome Trust Sanger Institute, Cambridge, UK)
Abstract :
One of the central processes in the cell is transcription, ie when the information stored in the DNA is read out in the form of messenger RNAs. Transcription typically involves a very small number of
molecules, and thus the law of mass action is no longer applicable. In the low molecule regime, fluctuations are no longer negligible, and a stochastic model is required.
The most rigorous approach is to use the Master Equation (ME), which is a difference-differential equation describing the probability of finding n molecules as a function of time. We present a closed form fully analytical solution of the ME for the most widely used model of gene expression. Analysis of the parameter space reveals a novel solution which has not been previously characterized. Using published data, we show that this regime is biologically relevant.
In the second part of the talk, I will discuss functional implications of gene expression noise. In particular, I will show that the mutual information between the promoter (input) and the mRNA level (output)
is proportional to the noise level. This proportionality implies that it is impossible to maximize the mutual information and to minimize the noise at the same time. Using genome-wide expression data, we demonstrate that the noise (and mutual information) in annotatedpathways are higher than expected by chance, suggesting that noise levels have been selected to allow for efficient signal propagation in the cell.