Abstract
The study of cellular metabolism is limited by the amount of experimental data available. Formulations able to extract relevant predictions from accessible measurements are needed. Maximum Entropy (ME) inference has been successfully applied to genome-scale models of cellular metabolism, and recent data-driven studies have suggested that in chemostat cultures of Escherichia coli (E. coli), the growth rate and uptake rates of limiting nutrients are the most informative observables. We propose the thesis that this can be explained by the chemostat dynamics, which typically drives nutrient-limited cultures toward observable metabolic states maximally restricted in the dimensions of those fluxes. A practical consequence is that relevant flux observables can now be replaced by culture parameters usually controlled. We test our model by using simulations, and then we apply it to E. coli experimental data where we evaluate the quality of the inference, comparing it to alternative formulations that rest on convex optimization.