{"title":"A neural ordinary differential equation model for predicting the growth of Chinese Hamster Ovary cell in a bioreactor system","authors":"Kuo-Chun Chiu, Dongping Du","doi":"10.1007/s12257-024-00141-2","DOIUrl":null,"url":null,"abstract":"<p>Chinese hamster ovary (CHO) cells play an important role in the biopharmaceutical industry, but their production efficiency requires enhancement to meet the growing market demands. Artificial intelligence (AI) has been a potent tool for modeling bioprocesses to support biopharmaceutical manufacturing. However, existing AI models do not adapt well to process data collected at irregular time intervals and have limited capability to scale up and down to incorporate various process parameters. To address the limitations, this study develops a novel neural ordinary differential equation (ODE) model for predicting key variables such as viable cell concentration, glucose concentration, lactate concentration, pH, and dissolved oxygen in a CHO cell bioreactor. Validated through extensive bioreactor experiments, the neural ODE model shows a better accuracy compared to the benchmark models, which include a conventional mechanistic model and a hybrid model. Additionally, the neural ODE model incorporated essential process variables that were not considered in the previous models. It successfully extrapolates to predict unknown dynamics at different initial conditions, which showcases robust adaptability. Moreover, the model provides useful insights into the relationship among variables, highlighting its potential for bioprocess modeling.</p>","PeriodicalId":8936,"journal":{"name":"Biotechnology and Bioprocess Engineering","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biotechnology and Bioprocess Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s12257-024-00141-2","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Chinese hamster ovary (CHO) cells play an important role in the biopharmaceutical industry, but their production efficiency requires enhancement to meet the growing market demands. Artificial intelligence (AI) has been a potent tool for modeling bioprocesses to support biopharmaceutical manufacturing. However, existing AI models do not adapt well to process data collected at irregular time intervals and have limited capability to scale up and down to incorporate various process parameters. To address the limitations, this study develops a novel neural ordinary differential equation (ODE) model for predicting key variables such as viable cell concentration, glucose concentration, lactate concentration, pH, and dissolved oxygen in a CHO cell bioreactor. Validated through extensive bioreactor experiments, the neural ODE model shows a better accuracy compared to the benchmark models, which include a conventional mechanistic model and a hybrid model. Additionally, the neural ODE model incorporated essential process variables that were not considered in the previous models. It successfully extrapolates to predict unknown dynamics at different initial conditions, which showcases robust adaptability. Moreover, the model provides useful insights into the relationship among variables, highlighting its potential for bioprocess modeling.
期刊介绍:
Biotechnology and Bioprocess Engineering is an international bimonthly journal published by the Korean Society for Biotechnology and Bioengineering. BBE is devoted to the advancement in science and technology in the wide area of biotechnology, bioengineering, and (bio)medical engineering. This includes but is not limited to applied molecular and cell biology, engineered biocatalysis and biotransformation, metabolic engineering and systems biology, bioseparation and bioprocess engineering, cell culture technology, environmental and food biotechnology, pharmaceutics and biopharmaceutics, biomaterials engineering, nanobiotechnology, and biosensor and bioelectronics.