Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks

Genome-scale metabolic networks are highly robust to the elimination of enzyme-coding genes. Their structure can evolve rapidly through mutations that eliminate such genes and through horizontal gene transfer that adds new enzyme-coding genes. Using flux balance analysis we study a vast space of metabolic network genotypes and their relationship to metabolic phenotypes, the ability to sustain life in an environment defined by an available spectrum of carbon sources. Two such networks typically differ in most of their reactions and have few essential reactions in common. Our observations suggest that the robustness of the Escherichia coli metabolic network to mutations is typical of networks with the same phenotype. We also demonstrate that networks with the same phenotype form large sets that can be traversed through single mutations, and that single mutations of different genotypes with the same phenotype can yield very different novel phenotypes. This means that the evolutionary plasticity and robustness of metabolic networks facilitates the evolution of new metabolic abilities. Our approach has broad implications for the evolution of metabolic networks, for our understanding of mutational robustness, for the design of antimetabolic drugs, and for metabolic engineering.Author Summary: Understanding the fundamental processes that shape the evolution of bacterial organisms is of general interest to biology and may have important applications in medicine. We address the questions of how bacterial organisms acquire innovations, including drug resistance, allowing them to survive in new environments. We simulate the evolution of the metabolic network, the network of reactions that can occur inside a living organism. The metabolic network of an organism depends on the genes contained in its genome and can change by gaining genes from other organisms through horizontal gene transfer or loss of gene activity through mutations. Our observations suggest that the robustness to gene loss in Escherichia coli is typical of random viable metabolic networks of the same size. We also find that metabolic networks can change significantly without causing the loss of an organism's ability to survive in a given environment. This property allows organisms to explore a wide range of novel metabolic abilities and is the source of their ability to innovate. Finally we present a method to find reactions that are essential across all organisms. Drugs targeting such a reaction may avoid drug resistance mutations that bypass the reaction.