Department of Chemical Engineering, Queen's University, 19 Division Street, Kingston, ON, K7L 3N6, Canada.
Institute for Systems Biology, 401 Terry Ave. N., Seattle, WA, 98109, USA.
Proteomics. 2020 Sep;20(17-18):e1900282. doi: 10.1002/pmic.201900282. Epub 2020 Jul 12.
Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.
Omic 技术使人们能够在不同的生物学尺度上全面读取细胞的分子状态。原则上，多种 omic 数据类型的组合可以提供整个生物系统的综合视图。这种集成需要系统生物学方法中的适当模型。在这里，基因组规模模型 (GEM) 作为一种计算系统生物学方法，用于解释和整合多组学数据。GEM 将生物体中发生的反应（与代谢、转录和翻译有关）转换为可以使用优化原理进行建模的数学公式。综述了用于解释多种 omic 数据类型的各种基因组规模建模方法，包括基因组学、转录组学、蛋白质组学、代谢组学和元组学。在生物系统背景下解释组学的能力为人类健康、环境生物技术、生物能源和代谢工程带来了重要发现。作者发现，随着 omic 技术的进步，基因组规模建模方法也在不断扩展，以实现对 omic 数据的更好解释。因此，预计通过将 omic 数据与 GEM 整合，继续综合有价值的知识。
