Grant: Discovery Grants Program - Individual

With the completion of numerous genome projects there is an increasing interest in understanding how molecules encoded within the genomes interact to define various functional networks of the cell. These intra-cellular networks may be classified as gene regulatory networks, protein interaction networks, or metabolic networks. Together these networks aim to describe the full set of reactions or molecular interactions between a genome and its environment, ultimately defining the growth and development of an organism. At the level of the cell, the chemical dynamics may well be dominated by the action of regulatory molecules that are present at levels of only a few copies per cell. Therefore, the molecular fluctuations of these components may determine the dynamics of the reaction network. These molecular fluctuations appear to have significant consequences, the observed large variation in rates of development, morphology and concentration of molecular species in a cell often lead to a randomization of phenotypic outcomes and non-genetic population heterogeneity. Since these fluctuations may have profound effects on the physiology of the cell, a stochastic model for the intra-cellular reaction networks and a statistical analysis appear to be necessary. Such networks of integrated molecular reactions tend to involve many different molecular species, thus posing a complex analytical problem. For prediction and simulation purposes it is essential to reduce both the model and computational complexity of the problem, while still capturing all of the essential characteristics and potential behavior of the network. The primary goal of this research program is to develop model reduction techniques for stochastic models of such intra-cellular reaction networks.