My research focuses on the development and refinement of constraint-based models of microbial metabolic and regulatory systems. Although existing metabolic and regulatory constraint-based models have been shown to make accurate predictions, there is still notable room for improvement. Many of the discrepancies between experimental observations and model predictions can be attributed to an incomplete picture of transcriptional regulation or metabolism. Consequently, the efficient collection of utilizable data is critical for improving the model’s predictive power and to discover deficiencies in the present knowledge relating to an organism’s metabolic and regulatory pathways. This type of information is essential to the overall performance of a constraint-based model which relies on databases of metabolism and regulation to make predictions regarding chemical production and cellular growth.
With this in mind, my current project involves determining how changes in the cell’s environment and genome affect coupling of growth and intracellular reactions. To investigate this problem, I have been developing an algorithm to select gene deletions and growth conditions that will force coupling between a given intracellular flux and biomass production. This algorithm is capable of coupling reactions for which gene-protein-reaction (GPR) relations are presently unknown and hence providing a potential means for determining these types of interactions. Such knowledge is invaluable for refining gene-knockout strategies that rely on these GPR relations (such as the OptOrf algorithm developed by the Reed lab).