In silico prediction of metabolic trait robustness in microbial cells

Microbial strains undergo notable biomass expansion in industrial applications subjecting populations to Darwinian selection. The consequent adaptive evolution may threaten the often tediously developed desired metabolic traits of the strains, such as flavor or platform chemical production. Yet, it remains unresolved how to predict the evolutionary trait robustness. Here, we propose TRAEV (Trait Robustness Against EVolution), a computational framework for in silico prediction of evolutionary robustness of metabolic traits and trajectories of desired traits in application environments. TRAEV uses constraint-based metabolic models for predicting environment-dependent trait-fitness dependencies and accounts for the stochasticity of adaptive evolution by using Monte Carlo-based perturbation analysis. First, TRAEV predicts the immediate metabolic phenotype in the new environment, and then, the subsequent evolutionary trajectories of fitness and desired trait. Metabolic trait robustness is quantified from the evolutionary trajectories as two scores: Robustness Score (RS) and Trade-off Score (TS). RS represents trait robustness and the overall trait level during evolutionary adaptation, whereas TS captures trait-fitness trade-offs. We validated TRAEV by predicting relative evolutionary robustness of heterologous pigmentation of two engineered Saccharomyces cerevisiae strains and two desired aroma generation traits by multiple wine strains of S. cerevisiae if they were developed via a laboratory selection process. Thus, we showed how TRAEV predictions could guide strain development. TRAEV can be integrated into computational strain design workflows across microbial strains, desired metabolic traits, and application environments. Ultimately, model-predicted evolutionary robustness of desired metabolic traits can guide both strain and process development and help enhancing the economic attractiveness of industrial applications using microbial cells.