Advances in reconstruction and optimization of cellular physiological metabolic network models.
- Author:
Luchi XIAO
1
;
Hongzhong LU
1
Author Information
- Publication Type:English Abstract
- Keywords: deep learning; enzyme-constrained genome-scale metabolic models (ecGEMs); genome-scale metabolic models (GEMs); kinetic models
- MeSH: Metabolic Networks and Pathways/physiology*; Models, Biological; Artificial Intelligence; Systems Biology; Kinetics; Cell Physiological Phenomena; Computational Biology; Synthetic Biology; Humans
- From: Chinese Journal of Biotechnology 2025;41(3):1112-1132
- CountryChina
- Language:Chinese
- 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.
