Anna Paola Muntoni — Politecnico di Torino #
An analytic approximation of the feasible space of metabolic networks #
The dynamics of chemical reactions inside a cell can be mathematically described by a set of ordinary differential equations for the concentrations of metabolites. Assuming a steady-state condition within a cell, concentrations are stationary and the rate at which reactions occur (called metabolic fluxes) satisfy an under-determined system of linear equations. Characterizing the space of fluxes that satisfy such equations along with given bounds (and possibly additional relevant constraints) is considered of utmost importance for the understanding of cellular metabolism. Extreme values for each individual flux can be computed with Linear Programming (as Flux Balance Analysis), and their marginal distributions can be approximately computed with Monte-Carlo sampling. Here we present an analytic method for the latter task based on a statistical mechanics inspired method, the Expectation Propagation approximation, that does not involve sampling and can achieve much better predictions than other existing analytic methods. With respect to sampling, we show that it has some advantages including computation time, and the ability to efficiently fix empirically estimated distributions of fluxes.