生物制造
计算生物学
比例(比率)
生物
基因组
计算机科学
遗传学
基因
物理
量子力学
作者
Seo‐Young Park,Dong‐Hyuk Choi,Jinsung Song,Meiyappan Lakshmanan,Anne Richelle,Seongkyu Yoon,Cleo Kontoravdi,Nathan E. Lewis,Dong‐Yup Lee
标识
DOI:10.1016/j.tibtech.2024.03.001
摘要
Abstract
Genome-scale metabolic models (GEMs) of Chinese hamster ovary (CHO) cells are valuable for gaining mechanistic understanding of mammalian cell metabolism and cultures. We provide a comprehensive overview of past and present developments of CHO-GEMs and in silico methods within the flux balance analysis (FBA) framework, focusing on their practical utility in rational cell line development and bioprocess improvements. There are many opportunities for further augmenting the model coverage and establishing integrative models that account for different cellular processes and data for future applications. With supportive collaborative efforts by the research community, we envisage that CHO-GEMs will be crucial for the increasingly digitized and dynamically controlled bioprocessing pipelines, especially because they can be successfully deployed in conjunction with artificial intelligence (AI) and systems engineering algorithms.
科研通智能强力驱动
Strongly Powered by AbleSci AI