TRAEV：用约束代谢模型在计算机中预测微生物细胞代谢性状的进化稳健性
<i>In silico</i> prediction of metabolic trait robustness in microbial cells
TRAEV 是一个基于约束代谢模型的计算框架，可在应用环境中预测代谢性状的进化稳健性及目标性状的演化轨迹。它先预测菌株在新环境中的即时代谢表型，再用 Monte Carlo 扰动分析纳入适应性进化的随机性，模拟适合度与目标性状随后的演化轨迹，并由此计算稳健性得分（RS）和权衡得分（TS）。作者用两株工程化 Saccharomyces cerevisiae 的异源色素合成，以及多株葡萄酒酵母的两种目标香气生成性状（假设经实验室选择流程开发）验证了 TRAEV 对相对进化稳健性的预测，表明其预测可指导菌株开发，并可整合进跨菌株、性状与应用环境的计算菌株设计流程。
来源：PLOS Computational Biology · journals.plos.org
by Chunhao Gu, Ville Mustonen, Paula Jouhten
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.