用代谢模型与机器学习预测并优化酵母产蛋白效率
An Integrative Genome-Scale Metabolic Modeling and Machine Learning Framework for Predicting and Optimizing Single-Cell Protein Production in Saccharomyces cerevisiae
- 整合酵母代谢模型与机器学习，预测不同条件下的蛋白质产量
- 通过贝叶斯优化使生物质通量提升12.13倍，达1.041 gDW/hr
- 适合合成生物学与工业发酵研究者参考
酿酒酵母（Saccharomyces cerevisiae）正成为单细胞蛋白（SCP）生产的重要来源，以应对全球蛋白质供应挑战。本研究提出一种整合基因组尺度代谢模型（Yeast9 GEM，含4,131个反应、2,806种代谢物、1,161个基因）与机器学习的计算框架，用于预测和优化SCP产量。基于2,000组拉丁超立方采样通量分布进行通量平衡分析（FBA），随机森林与XGBoost回归器的R²分别达到0.9999760和0.9997702。变分自编码器（VAE）识别出四个代谢簇，平均生物质通量分别为0.472、0.493、0.527和0.505 gDW/hr。SHAP特征归因揭示糖酵解、TCA循环及氨基酸生物合成中的20条关键反应，其中18条（90%）在体外敲除实验中被验证为必需。贝叶斯优化使生物质通量从0.0858提升至1.041 gDW/hr（12.13倍），最优条件为葡萄糖=-20.0、氧气=-20.0、铵=-8.9 mmol/gDW/hr。生成对抗网络（GAN）生成的新通量配置方差为0.124，但100个样本中无一满足化学计量可行性，归因于生成器未完全收敛，列为局限。帕累托前沿分析确定最优操作点：生物质通量0.0858 gDW/hr，氨基酸生物合成评分为1000.029 mmol/gDW/hr。
原文摘要 · Abstract (English)
Saccharomyces cerevisiae is increasingly recognised as a key source for single-cell protein (SCP) production, a rising solution to global protein-supply challenges. This study presents a computational framework combining the Yeast9 genome-scale metabolic model (GEM) with machine learning and optimisation to predict and enhance biomass flux for SCP yield. The Yeast9 GEM, comprising 4,131 reactions, 2,806 metabolites, and 1,161 genes, was simulated using flux balance analysis (FBA) across 2,000 Latin Hypercube-sampled flux profiles. Random Forest and XGBoost regressors achieved R2 values of 0.9999760 and 0.9997702, respectively. A variational autoencoder (VAE) identified four metabolic clusters with mean biomass fluxes of 0.472, 0.493, 0.527, and 0.505 gDW/hr. SHAP-based feature attribution identified twenty key reactions in glycolysis, the TCA cycle, and amino-acid biosynthesis; 18/20 (90%) were confirmed essential by in silico knockout. Bayesian optimisation produced a 12.13-fold improvement in biomass flux (0.0858 to 1.041 gDW/hr) at glucose = -20.0, oxygen = -20.0, and ammonium = -8.9 mmol/gDW/hr. A generative adversarial network (GAN) generated novel flux configurations (variance = 0.124); stoichiometric feasibility verification returned 0/100 feasible profiles due to incomplete generator convergence, reported as a limitation. Pareto front analysis identified an optimal SCP operating point at 0.0858 gDW/hr biomass flux with amino-acid biosynthesis score of 1000.029 mmol/gDW/hr.
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