Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden; Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
Curr Opin Biotechnol. 2024 Apr;86:103078. doi: 10.1016/j.copbio.2024.103078. Epub 2024 Feb 15.
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.
单细胞技术已广泛应用于生物学研究，并产生了大量需要解释的单细胞数据。由于包含先验代谢网络知识以及基因-蛋白质-反应关联，基于基因组规模的代谢模型（GEMs）已成为整合并解释主要来自于大量样本的各种组学数据的有力工具。在这里，我们首先回顾了两种利用 GEMs 进行大量组学数据的常用方法，然后讨论了利用 GEMs 进行单细胞组学数据的综合分析的进展。最后，我们提出了对该领域当前挑战和前景的看法。
