We are interested in deciphering the activity of biochemical networks, and in particular the genome-scale metabolic network reconstructions. The metabolic network of a cell is often at steady-state with no drastic changes in metabolite concentrations assuming a fixed external environment. Flux Balance Analysis (FBA) is a widely used predictive model which computes a cell's steady-state chemical reaction fluxes as a solution to an optimization problem with constraints that capture stoichiometry mass balances and the composition of the growth medium. FBA, however, assumes a certain global cellular objective function which is not necessarily known. Understanding its structure can elucidate the cellular metabolic control mechanisms and infer important information regarding an organism's evolution. To that end, I will present a general framework for model estimation from observed equilibria. In metabolic networks, for instance, reaction fluxes of the cells under specific growth conditions can be measured. In other types of networks, such as transportation networks, users' congestion function are not typically known but equilibria (traffic flows) can easily be measured. Our framework allows for both parametric and non-parametric estimation and provides probabilistic guarantees on the quality of the estimated quantities. I will apply this general framework to estimating cellular objectives in metabolic networks.I will present results that show good agreement with simulated E. coli data, time-dependent flux estimates inferred from gene expression data, and experimental flux measurements in long-term evolved E. coli strains. The latter data, in particular, reveal cellular objective functions that provide insight into possible metabolic adaptation trajectories.