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Driving towards digital biomanufacturing by CHO genome-scale models

  • Seo Young Park
  • , Dong Hyuk Choi
  • , Jinsung Song
  • , Meiyappan Lakshmanan
  • , Anne Richelle
  • , Seongkyu Yoon
  • , Cleo Kontoravdi
  • , Nathan E. Lewis
  • , Dong Yup Lee
  • Sungkyunkwan University
  • Indian Institute of Technology Madras
  • Sartorius Corporate Research
  • University of Massachusetts Lowell
  • Imperial College London
  • University of California at San Diego

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish
Pages (from-to)1192-1203
Number of pages12
JournalTrends in Biotechnology
Volume42
Issue number9
DOIs
StatePublished - Sep 2024

Keywords

  • bioprocess digital twins
  • Chinese hamster ovary cells
  • digital biomanufacturing
  • flux balance analysis
  • genome-scale metabolic models
  • recombinant therapeutic proteins

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