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박서영Seo-Young Park

성균관대학교

Trends Biotechnol., Mar 27 2024, S0167-7799(24)00065-9 | https://doi.org/10.1016/j.tibtech.2024.03.001 Driving towards digital biomanufacturing by CHO genome-scale models

Seo-Young Park ^1,7, Dong-Hyuk Choi ^1,7, Jinsung Song ¹, Meiyappan Lakshmanan ², Anne Richelle ³, Seongkyu Yoon⁴, Cleo Kontoravdi ⁵, Nathan E. Lewis ⁶, Dong-Yup Lee ¹

¹School of Chemical Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea

²Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, and Centre for Integrative Biology and Systems Medicine (IBSE), Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India

³Sartorius Corporate Research, Avenue Ariane 5, 1200 Brussels, Belgium

⁴Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA 01850, USA

⁵Department of Chemical Engineering and Chemical Technology, Imperial College London, South Kensington Campus, London SW7 2AZ, UK

⁶Departments of Pediatrics and Bioengineering, University of California San Diego, La Jolla, CA 92093, USA

⁷Equal contributions

Correspondence: Dong-Yup Lee

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.

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형식Review Paper
게재일2024년 03월 (BRIC 등록일 2024-04-01)
연구진국내(교신)+국외 연구진
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