Phenotypic constraints promote latent versatility and carbon efficiency in metabolic networks

TL;DR

This study demonstrates that direct selective pressures for growth in specific environments shape metabolic networks to enhance their latent versatility and carbon efficiency, suggesting these properties may arise from nonadaptive processes.

Contribution

The paper shows how constraints on metabolic networks influence their versatility and efficiency, highlighting nonadaptive origins of these properties through computational sampling.

Findings

Latent versatility increases with constrained environments and network size.

Carbon wastage decreases as the number of constrained environments and network size grow.

Metabolic network properties can emerge from nonadaptive processes.

Abstract

System-level properties of metabolic networks may be the direct product of natural selection or arise as a by-product of selection on other properties. Here we study the effect of direct selective pressure for growth or viability in particular environments on two properties of metabolic networks: latent versatility to function in additional environments and carbon usage efficiency. Using a Markov Chain Monte Carlo (MCMC) sampling based on Flux Balance Analysis (FBA), we sample from a known biochemical universe random viable metabolic networks that differ in the number of directly constrained environments. We find that the latent versatility of sampled metabolic networks increases with the number of directly constrained environments and with the size of the networks. We then show that the average carbon wastage of sampled metabolic networks across the constrained environments decreases with the number of directly constrained environments and with the size of the networks. Our work expands the growing body of evidence about nonadaptive origins of key functional properties of biological networks.