Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes resulting from stochastic gene expression. We are developing methods to quantify the unique stochastic variability patterns of different biological systems in order to infer and model the relationships between genes within the underlying regulatory networks. Specifically, we are developing sophisticated image processing and experimental approaches to measure spatial-temporal expression patterns of multiple RNA species within the same cell. From these data, we can derive multidimensional probability distributions that provide insight into the structure of gene regulatory networks. In essence, these cell-to-cell variability patterns are unique ‘fingerprints’ that reveal information about the relationships between genes within regulatory networks. The benefit of this approach is that the biological system being studied does not have to be genetically manipulated, making it particularly powerful to study mammalian gene regulatory networks.
