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However, achieving higher recombinant protein titers remains an ongoing challenge and a fundamental understanding of the cellular mechanism driving improved bioprocess performance remains elusive. To directly address these challenges and achieve substantial improvements, a more in\u2010depth understanding of cellular function within a bioprocess environment may be required. Over the past decade, significant advancements have been made in the building of genome\u2010scale metabolic models (GEMs) for CHO cells, bridging the gap between high information content 'omics data and the ability to perform\n                    <jats:italic>in silico<\/jats:italic>\n                    phenotypic predictions. Here, time\u2010course transcriptomics has been employed to constrain culture phase\u2010specific GEMs, representing the early exponential, late exponential, and stationary\/death phases of CHO cell fed\u2010batch bioreactor culture. Temporal bioprocess data, including metabolite uptake and secretion rates, as well as growth and productivity, has been used to validate flux sampling results. Additionally, high mAb\u2010producing solutions have been identified and the metabolic signatures associated with improved mAb production have been hypothesized. Finally, constraint\u2010based modeling has been utilized to infer specific amino acids, cysteine, histidine, leucine, isoleucine, asparagine, and serine, which could drive increased mAb production and guide optimal media and feed formulations.\n                  <\/jats:p>","DOI":"10.1002\/bit.28982","type":"journal-article","created":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T02:05:46Z","timestamp":1744423546000},"page":"1898-1913","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Flux Sampling Suggests Metabolic Signatures of High Antibody\u2010Producing CHO Cells"],"prefix":"10.1002","volume":"122","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1439-6302","authenticated-orcid":false,"given":"Kate E.","family":"Meeson","sequence":"first","affiliation":[{"name":"Faculty of Biology, Medicine and Health University of Manchester Manchester UK"},{"name":"Manchester Institute of Biotechnology University of 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