Applications of machine learning in the reconstruction and curation of genome-scale metabolic models

WU Ke; LUO Jiahao; LI Feiran

doi:10.12211/2096-8280.2024-090

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Applications of machine learning in the reconstruction and curation of genome-scale metabolic models

Invited Review | 更新时间：2025-07-07

- Applications of machine learning in the reconstruction and curation of genome-scale metabolic models
- Synthetic Biology Journal Vol. 6, Issue 3, Pages: 566-584(2025)
- 作者机构：
  
  1.清华大学深圳国际研究生院，生物医药与健康工程研究院，广东深圳 518055
  2.清华大学化学工程系，工业生物催化教育部重点实验室，北京 100084
- 作者简介：
- 基金信息：
- DOI：10.12211/2096-8280.2024-090
  CLC： Q814.9
- Received：02 December 2024，
  
  Revised：2025-02-12，
  
  Published：30 June 2025
- 稿件说明：
移动端阅览
吴柯，罗家豪，李斐然. 机器学习驱动的基因组规模代谢模型构建与优化［J］. 合成生物学， 2025， 6（3）： 566-584

WU Ke， LUO Jiahao， LI Feiran. Applications of machine learning in the reconstruction and curation of genome-scale metabolic models［J］. Synthetic Biology Journal， 2025， 6（3）： 566-584
吴柯，罗家豪，李斐然. 机器学习驱动的基因组规模代谢模型构建与优化［J］. 合成生物学， 2025， 6（3）： 566-584 DOI： 10.12211/2096-8280.2024-090.

WU Ke， LUO Jiahao， LI Feiran. Applications of machine learning in the reconstruction and curation of genome-scale metabolic models［J］. Synthetic Biology Journal， 2025， 6（3）： 566-584 DOI： 10.12211/2096-8280.2024-090.

摘要

自1999年首个基因组规模代谢模型（genome-scale metabolic model，GEM）问世以来，GEM已成为解析生物代谢的重要工具。该模型包含代谢基因、代谢物和反应，并结合化学计量矩阵与约束优化，系统描述和模拟生物体内的代谢过程。此外，GEM能够整合热力学参数、动力学参数、多组学数据及多细胞过程，构建更精细且具有更强大预测能力的多约束多过程模型。然而，先验知识的局限成为其发展的瓶颈。机器学习技术凭借强大的数据处理和模式识别能力，为进一步扩展GEM提供了新思路。本综述系统总结了传统GEM及多约束多过程模型的构建流程，并着重探讨了机器学习在其中关键步骤中的应用前景，如基因功能注释、途径解析、空缺填补和生物学参数预测。机器学习技术作为新的驱动力，有望大幅度提升GEM的规模和质量，深化对生物代谢机制的理解，并推动实现数字孪生细胞。

Abstract

Since the publication of the first genome-scale metabolic model (GEM) in 1999

GEMs have become an essential tool for analyzing metabolism. The models integrate genes

metabolites

and reactions for combining stoichiometric matrices with constraint-based optimization to systematically describe and simulate metabolic processes in organisms. The development of automated pipelines for reconstructing GEMs has expanded their applicability to organisms from all kingdoms of life. Additionally

GEMs can integrate kinetic parameters

thermodynamic parameters

multi-omics data and multi-cellular processes to reconstruct more accurate models

thereby improving prediction accuracy. However

the reconstruction of GEMs remains heavily dependent on pre-existing knowledge

inherently limiting their scope to currently available information. This dependency restricts our ability to fully unravel the complexity and dynamic nature of metabolism. Recent advances in machine learning have demonstrated extraordinary capabilities for biological tasks such as protein structure prediction

disease identification and GEM reconstruction with functional annotation and large-scale data integration

showcasing its power in identifying patterns and uncovering hidden relationships within biological systems. Machine learning provides a promising pathway to overcome the limitations of GEMs by expanding their applicability to areas previously constrained by data availability and complexity. This review summarizes the traditional reconstruction methods of GEMs and their applications in integrating multi-dimensional data to build multi-constraint and multi-process models. The review also focuses on key applications of machine learning in gene function annotation

pathway analysis

gap-filling prediction in the reconstruction of GEMs. Additionally

the potential of machine learning in predicting kinetic

thermodynamic

and other key biochemical parameters in the reconstruction of multi-constraint and multi-process models is discussed. By combining GEMs with machine learning innovations

researchers can improve model accuracy

enhance scalability

and gain new insights into previously elusive metabolic mechanisms

bridging gaps in metabolic knowledge

and underscoring its importance as a cornerstone for future development in systems biology and biotechnology.

关键词

Keywords

references

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