van Oudenaarden: Quantitative biology

Stochastic gene expression
Within the confines of individual cells, minute changes in the concentration or spatial arrangement of molecules can produce substantial effects. For example, a transcription factor equally prevalent in two isogenic cells might be bound to a promoter in one and unbound in another, subject to the dictates of statistical mechanics. Protein production would consequently begin in one cell and not the other, amplifying the fluctuation, and propelling each cell to a different fate. Identical genotype and an identical growth environment are thus insufficient to ensure that two cells will develop the same phenotypes. A major goal of our research has been to identify and differentiate between the myriad possible origins of this variability, understand which are biologically important, which are not, and to put firm numbers on each of them.

MicroRNAs
MicroRNAs (miRNAs) are short, highly conserved non-coding RNA molecules that repress gene expression in a sequence-dependent manner. MiRNAs regulate protein synthesis in the cell cytoplasm by promoting target mRNAs’ degradation and/or inhibiting their translation. Their importance is suggested by the predictions that each miRNA targets hundreds of genes and that the majority of protein-coding genes are miRNA targets; by their abundance, with some miRNAs expressed as high as 50,000 copies per cell; and by their sequence conservation, with some miRNAs conserved from sea urchins to humans.

MiRNAs can regulate a large variety of cellular processes, from differentiation and proliferation to apoptosis. MiRNAs also confer robustness to systems by stabilizing gene expression during stress and in developmental transitions. In our lab we use a combination of quantitative single cell experiments and computer/in silico models to better understand miRNA regulation. We are particularly interested in how miRNAs can generate thresholds in target gene expression and mediate feedforward and feedback loops in gene networks.