Precise metabolic modeling in post-omics era: accomplishments and perspectives.

IF 8.1 2区 工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Yawen Kong, Haiqin Chen, Xinlei Huang, Lulu Chang, Bo Yang, Wei Chen
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引用次数: 0

Abstract

Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.

后组学时代的精确代谢建模:成就与展望。
微生物在合成所需生物产品方面具有可持续和可扩展的特性,因此被广泛应用。然而,对细胞内新陈代谢的认识不足阻碍了微生物的进一步应用。基因组尺度代谢模型(GEM)在促进全面了解细胞代谢机制方面发挥着关键作用。这些模型通过探索微生物的代谢途径和预测潜在靶标,实现了合理的改造,从而在不增加实验成本的情况下对细胞进行精确调控。然而,简化的 GEM 只考虑了基因组信息和网络化学计量,而忽略了其他重要的生物信息,如酶功能、热力学特性和动力学参数。因此,特别是在预测复杂多变系统中的微生物行为时,不确定性依然存在。全息时代的到来,对各种条件下的基因、蛋白质和代谢物进行了大量量化,导致多约束模型和更新算法的蓬勃发展,它们提高了预测能力,拓宽了维度。同时,机器学习(ML)在应用于生物大数据的训练集时,已显示出卓越的分析和预测能力。将 ML 的判别优势与 GEM 相结合,有助于提高机理建模效率和预测准确性。本文概述了 GEM 的研究创新,包括多约束建模、分析方法和 ML 的最新应用,这些创新可为广泛的生物大分子的遗传改良、菌株开发和产量提高提供全面的知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Critical Reviews in Biotechnology
Critical Reviews in Biotechnology 工程技术-生物工程与应用微生物
CiteScore
20.80
自引率
1.10%
发文量
71
审稿时长
4.8 months
期刊介绍: Biotechnological techniques, from fermentation to genetic manipulation, have become increasingly relevant to the food and beverage, fuel production, chemical and pharmaceutical, and waste management industries. Consequently, academic as well as industrial institutions need to keep abreast of the concepts, data, and methodologies evolved by continuing research. This journal provides a forum of critical evaluation of recent and current publications and, periodically, for state-of-the-art reports from various geographic areas around the world. Contributing authors are recognized experts in their fields, and each article is reviewed by an objective expert to ensure accuracy and objectivity of the presentation.
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