Multidimensional Optimality of Microbial Metabolism

TLDR

While metabolic network topology is well mapped, the principles governing flux distribution remain poorly understood. Experimentally, fluxes are measured by (13)C‑flux analysis, and using nine bacterial flux datasets with multi‑objective optimization theory, the authors show that metabolism operates near the Pareto‑optimal surface of a three‑dimensional space defined by competing objectives. The analysis reveals that metabolic flux states evolve under a trade‑off between optimality in a given condition and minimal adjustment across conditions, suggesting these forces shape microbial fluxes in their environmental context.

Abstract

Although the network topology of metabolism is well known, understanding the principles that govern the distribution of fluxes through metabolism lags behind. Experimentally, these fluxes can be measured by (13)C-flux analysis, and there has been a long-standing interest in understanding this functional network operation from an evolutionary perspective. On the basis of (13)C-determined fluxes from nine bacteria and multi-objective optimization theory, we show that metabolism operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Consistent with flux data from evolved Escherichia coli, we propose that flux states evolve under the trade-off between two principles: optimality under one given condition and minimal adjustment between conditions. These principles form the forces by which evolution shapes metabolic fluxes in microorganisms' environmental context.