Metabolic networks are among the most conserved and best understood biological networks. Yet, deciphering how metabolism at the cellular level responds to genetic (e.g. gene deletion) and environmental (e.g. nutrient shift) perturbations is an open challenge, relevant both to understanding physiological regulation and evolutionary adaptation. In our group, we approach these questions using, among others, cell-scale flux balance models of metabolic networks. In flux balance models, metabolic networks are treated as steady state systems, whose reaction rates (fluxes) can span a space of solutions constrained by fundamental mass conservation laws. Efficient optimization algorithms can search this space for flux arrangements that optimize a given objective function, such as maximization of cellular growth, or minimization of the deviation of fluxes with respect to a previously achieved state [Segrè et al., PNAS 2002]. Using these approaches, we can perform large-scale computer experiments of single and double gene deletions, and represent the results in the form of a network of epistatic interactions (e.g. synthetic lethality) between genes, which provide valuable information about the modular structure of the underlying biochemical network [Segrè et al., Nature Genetics 2005].

Predicting selection and epistasis in microbial systems

Even for small systems, such as bacterial cells, the genetic and biochemical circuits on which evolutionary adaptation operates are often so complex, that it is almost impossible to predict by simple inspection and intuition how changes in network wiring and parameters will affect fitness. As part of an interdisciplinary collaboration with the Marx Laboratory, we are developing mathematical models and computer simulations to help understand the physiological basis of evolutionary adaptation and epistasis in the methylotrophic bacterium Methylobacterium extorquens. By combining kinetic models and steady state constraint-based models we seek to provide Mechanistic explanation of beneficial mutations, and to predict possible evolutionary paths and epistatic interactions.