Single-cell omics analysis with genome-scale metabolic modeling

IF 7.1 2区工程技术 Q1 BIOCHEMICAL RESEARCH METHODS

Current opinion in biotechnology Pub Date : 2024-02-15 DOI:10.1016/j.copbio.2024.103078

Yu Chen , Johan Gustafsson , Jingyu Yang , Jens Nielsen , Eduard J Kerkhoven

引用次数: 0

Abstract

Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene–protein–reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.

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利用基因组尺度代谢模型进行单细胞全微观分析

单细胞技术已广泛应用于生物学研究，并产生了大量有待解读的单细胞数据。由于包含了先验代谢网络知识以及基因-蛋白质反应关联，基因组尺度代谢模型（GEMs）已成为整合并解释各种omics数据（主要来自大样本）的有力工具。在此，我们首先回顾了利用 GEMs 利用大容量全息数据的两种常见方法，然后讨论了利用 GEMs 综合分析单细胞全息数据的进展。最后，我们将就这一领域当前面临的挑战和前景发表自己的看法。

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

Current opinion in biotechnology 工程技术-生化研究方法

CiteScore

16.20

自引率

2.60%

发文量

226

审稿时长

4-8 weeks

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