UC San Diego

Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are poorly understood. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set ('underground metabolism') can aid in forecasting the genetic basis of metabolic adaptations. In this dissertation, novel approaches toward exploring promiscuity in the space of a metabolic network are described. The work leverages genome-scale models, which have been widely used for predicting growth phenotypes in various nutrient environments and following genetic perturbation in Escherichia coli. Failure modes of model predictions in relation to gene essentiality are explored as opportunities for targeting biological discovery, suggesting the presence of unknown underground pathways stemming from enzymatic cross-reactivity or suggesting limitations of experimental conditions stemming from short growth tests. Workflows are presented that couple constraint-based modeling and bioinformatic tools with knockout strain analysis and long-term growth experiments for the purpose of enhancing knowledge and predictability of enzyme promiscuity at the genome scale. Furthermore, a computational model of underground metabolism and laboratory evolution experiments are employed to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates. Promiscuous enzyme activities played key roles in multiple phases of adaptation. Genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity. Thus, it is shown that computational approaches will be essential to synthesize the complex role of promiscuous activities in models of evolutionary adaptation.