Saccharomyces cerevisiae is a cornerstone organism in industrial biotechnology, valued for its genetic tractability and robust fermentative capacity. Accurately predicting biomass flux across diverse environmental and genetic perturbations remains a significant challenge for rational strain design. We present a computational framework combining the Yeast9 genome-scale metabolic model with machine learning and optimization to predict, interpret, and enhance biomass flux. Flux balance analysis generated 2,000 flux profiles by varying glucose, oxygen, and ammonium uptake rates. Random Forest and XGBoost regressors achieved R2 of 0.99989 and 0.9990, respectively. A variational autoencoder revealed four distinct metabolic clusters, and SHAP analysis identified glycolysis, the TCA cycle, and lipid biosynthesis as key biomass determinants. In silico overexpression achieved a biomass flux of 0.979 gDW/hr, while Bayesian optimization of nutrient constraints produced a 12-fold increase (0.0858 to 1.041 gDW/hr). A generative adversarial network proposed stoichiometrically feasible novel flux configurations. This framework demonstrates how genome-scale simulation, interpretable ML, and generative modeling can advance yeast metabolic engineering.
用于预测和优化酿酒酵母生物燃料相关生物质生产的整合性基因组尺度代谢建模与机器学习框架 / An Integrative Genome-Scale Metabolic Modeling and Machine Learning Framework for Predicting and Optimizing Biofuel-Relevant Biomass Production in Saccharomyces cerevisiae
本研究开发了一个结合基因组尺度代谢模型与机器学习的新计算框架，能够高精度预测并显著优化酿酒酵母的生物质产量，为高效生产生物燃料的菌株设计提供了新工具。
源自 arXiv: 2603.25561
