Combining Knowledge About Metabolic Networks and Single-Cell Data with Maximum Entropy

Better understanding of the fitness and flexibility of microbial platform organisms is central to biotechnological process development. Live-cell experiments uncover the phenotypic heterogeneity of living cells, emerging even within isogenic cell populations. However, how this observed heterogeneity in growth relates to the variability of intracellular processes that drive cell growth and division is less understood. We here approach the question, how the observed phenotypic variability in single-cell growth rates links to metabolic processes, specifically intracellular reaction rates (fluxes). To approach this question, we employ the Maximum Entropy (<i>MaxEnt</i>) principle that allows us to bring together the phenotypic solution space, derived from metabolic network models, to single-cell growth rates observed in live-cell experiments. We apply the computational machinery to first-of-its-kind data of the microorganism <i>Corynebacterium glutamicum</i>, grown on different substrates under continuous medium supply. We compare the MaxEnt-based estimates of metabolic fluxes with estimates obtained by assuming that the average cell operates at its maximum growth rate, which is the current predominant practice in biotechnology.