Controlling and Exploiting Stochasticity in Gene Regulatory Networks

The precision with which cells undergo differentiation programs or respond to external stimuli is remarkable, especially when considering the inherently "noisy" molecular interactions underlying these processes. StoNets aims to understand how stochasticity is controlled - and even exploited - to allow the development of robust behaviors of genetic networks, cells and cellular systems.

Although all cells within an organism carry the same genetic information, regulatory mechanisms that operate at essentially all steps of gene expression lead to a large variety of cell types and behaviors. Progress in measurement technologies has enabled the precise and high-throughput probing of molecules and cells. This in turn has revealed that stochasticity is pervasive in all gene expression regulatory systems. The central question we will address in this StoNets project is how robust and reproducible cellular behaviors can emerge in spite of these noisy molecular interactions.

A "slice" across the levels of gene expression organization

We will undertake a systematic investigation into the mechanisms that have emerged to control noise at different organizational scales of gene expression regulation, from transcription of individual genes to cell fate switching. Each of the example systems that we selected is interesting in its own right and exhibits important stochastic aspects. 

Questions motivated by modeling

With the continuous progress in understanding the basic mechanisms of gene regulation, many key molecular players have been identified and numerous direct regulatory interactions have been mapped. Theoreticians have started to model the way in which specific behaviors emerge from the underlying interactions. These efforts have led to a large number of new, inherently quantitative questions regarding the biological systems. Through a very tight integration of experimental and computational approaches StoNets aims to answer such questions ultimately contributing to an improved, quantitative understanding and thereby controllability of cellular behaviors.

Updated August 2013