细胞生理代谢网络模型重构和优化的进展

Advances in reconstruction and optimization of cellular physiological metabolic network models

摘要:

细胞中自发或由酶催化的代谢反应组成了高度复杂的代谢网络，其与细胞生理代谢活动运作密切相关。细胞生理代谢网络模型的重构有助于从系统层面上解析基因型与生长表型之间的关联，为细胞生理代谢活动精准刻画与生物绿色制造等研究提供重要的计算生物学工具。本文系统介绍了全基因组尺度代谢网络模型(genome-scale metabolic models, GEMs)、动力学模型、酶约束代谢模型(enzyme-constrained genome-scale metabolic models, ecGEMs)等不同类型细胞生理代谢网络模型发展与应用的最新研究进展；同时还介绍了GEMs自动化构建研究进展以及条件特异性GEMs建模策略。人工智能技术为高精度细胞生理代谢网络模型构建提供了全新机遇，本文进一步总结了人工智能技术在动力学模型和酶约束模型构建等领域的应用。各类细胞生理代谢网络模型的高质量重构将为今后的定量合成生物学与系统生物学等研究提供强大计算支撑。

关键词:基因组尺度代谢网络模型;动力学模型;酶约束代谢模型;深度学习

Abstract:

The metabolic reactions in cells, whether spontaneous or enzyme-catalyzed, form a highly complex metabolic network closely related to cellular physiological metabolic activities. The reconstruction of cellular physiological metabolic network models aids in systematically elucidating the relationship between genotype and growth phenotype, providing important computational biology tools for precisely characterizing cellular physiological metabolic activities and green biomanufacturing. This paper systematically introduces the latest research progress in different types of cellular physiological metabolic network models, including genome-scale metabolic models (GEMs), kinetic models, and enzyme-constrained genome-scale metabolic models (ecGEMs). Additionally, our paper discusses the advancements in the automated construction of GEMs and strategies for condition-specific GEM modeling. Considering artificial intelligence offers new opportunities for the high-precision construction of cellular physiological metabolic network models, our paper summarizes the applications of artificial intelligence in the development of kinetic models and enzyme-constrained models. In summary, the high-quality reconstruction of the aforementioned cellular physiological metabolic network models will provide robust computational support for future research in quantitative synthetic biology and systems biology.

Key words:genome-scale metabolic models (GEMs);kinetic models;enzyme-constrained genome-scale metabolic models (ecGEMs);deep learning