A neural ordinary differential equation model for predicting the growth of Chinese Hamster Ovary cell in a bioreactor system

IF 2.5 4区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Biotechnology and Bioprocess Engineering Pub Date : 2024-09-01 DOI:10.1007/s12257-024-00141-2
Kuo-Chun Chiu, Dongping Du
{"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.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
预测中国仓鼠卵巢细胞在生物反应器系统中生长的神经常微分方程模型
中国仓鼠卵巢(CHO)细胞在生物制药行业中发挥着重要作用,但其生产效率需要提高才能满足日益增长的市场需求。人工智能(AI)一直是建立生物过程模型以支持生物制药生产的有效工具。然而,现有的人工智能模型并不能很好地适应以不规则时间间隔收集的工艺数据,并且在纳入各种工艺参数方面的放大和缩小能力有限。为了解决这些局限性,本研究开发了一种新型神经常微分方程(ODE)模型,用于预测 CHO 细胞生物反应器中的存活细胞浓度、葡萄糖浓度、乳酸浓度、pH 值和溶解氧等关键变量。通过大量的生物反应器实验验证,神经 ODE 模型与基准模型(包括传统机械模型和混合模型)相比显示出更高的准确性。此外,神经 ODE 模型还纳入了以往模型未考虑的基本过程变量。它成功地推断预测了不同初始条件下的未知动态,展示了强大的适应性。此外,该模型对变量之间的关系提供了有用的见解,突出了其在生物过程建模方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Biotechnology and Bioprocess Engineering
Biotechnology and Bioprocess Engineering 工程技术-生物工程与应用微生物
CiteScore
5.00
自引率
12.50%
发文量
79
审稿时长
3 months
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.
期刊最新文献
Assessing the applicability of tunicate skin-extracted cellulose as a base material for ultrasound gel Fabrication of protein–inorganic biohybrid as an imageable drug delivery system comprising transferrin, green fluorescent protein, and copper phosphate Continuous cell recycling in methylotrophic yeast Pichia pastoris to enhance product yields: a case study with Yarrowia lipolytica lipase Lip2 Sensitive detection of SARS-CoV2 spike antibodies by a paper-based polypyrrole/reduced graphene oxide sensor A neural ordinary differential equation model for predicting the growth of Chinese Hamster Ovary cell in a bioreactor system
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1