Intelligent design of metabolic pathways in microbial cell factories based on AI prediction
基于AI预测的微生物细胞工厂代谢通路智能设计

摘要

基于AI预测的微生物细胞工厂代谢通路智能设计，通过整合多组学数据与机器学习模型，实现代谢流的精准预测与优化，提升目标产物的合成效率。该研究以代谢网络建模为核心，结合深度学习算法对关键节点进行识别与调控策略设计，从而构建高效、稳定的细胞工厂。在实验验证环节，通过对特定代谢通路的改造，显著提高了产物产率与转化效率，验证了AI预测在指导代谢工程中的可行性与优越性。本方法不仅为工业生物制造提供了新的智能化设计思路，还为未来的绿色可持续生产奠定了技术基础。

Abstract

The intelligent design of metabolic pathways in microbial cell factories based on AI prediction achieves accurate prediction and optimization of metabolic flux by integrating multi-omics data with machine learning models, thereby improving the synthesis efficiency of target products. Centered on metabolic network modeling, this study combines deep learning algorithms to identify key nodes and design regulatory strategies, thus constructing efficient and stable cell factories. In the experimental verification stage, the modification of specific metabolic pathways significantly improved product yield and conversion efficiency, verifying the feasibility and superiority of AI prediction in guiding metabolic engineering. This method not only provides a new intelligent design idea for industrial biomanufacturing but also lays a technical foundation for future green and sustainable production.