在基因组规模的代谢模型中整合多omics:对基于约束的方法的审查
Nabia Shahreen1, Abraham Osinuga1, Sunayana Malla1
1Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA.
Molecular omics
|February 9, 2026
概括
本综述通过约束逻辑组织了基因组规模代谢模型 (GEM) 的多omics整合策略. 它概述了各种领域可复制和有生物意义的应用程序的实际工作流程.
科学领域:
- 系统生物学 系统生物学
- 代谢工程是代谢工程.
背景情况:
- 基因组规模的代谢模型 (GEMs) 已经从静态度重建演变为预测性,限制意识的平台.
- 整合多主题数据对于提高GEM的预测能力和生物相关性至关重要.
研究的目的:
- 根据它们所施加的约束逻辑,在GEM中组织多omics集成策略.
- 提供一个连贯的框架,使GEM可复制,可移植和具有生物意义.
主要方法:
- 通过约束逻辑对omics集成策略进行分类 (例如,生物质函数,转录原子开关,酶/表达,蛋白质组预算,热力学,流量学).
- 整合机器学习来推断缺失的数据,同时保持机械结构.
- 开发实用的工作流程,包括受酶约束的建模,热力学嵌入和流动学引导的校准.
主要成果:
- 基于约束架构的多omics集成策略的系统组织.
- 定义了实际的工作流程和最低报告标准,以确保透明度和可重复性.
- 确定了新兴方向,例如单细胞/空间数据集成和先进的机器学习方法.
结论:
- 通过约束架构构构建omics集成框架,为推进GEM提供了一个统一的议程.
- 这种方法促进了GEM在生物技术,医学,农业和环境科学领域的应用.
- 强调可重复性,可移植性和生物意义是未来发展的关键.
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