多细胞系学习用于以数据为导向的机械代谢模型的构建
Yen-An Lu1, Meghan G McCann1, Wei-Shou Hu1
1Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota, USA.
Biotechnology and bioengineering
|June 4, 2024
概括
这项研究引入了哺乳动物细胞培养中的代谢建模的新框架,结合了酶活性和基因表达动态. 这种方法提高了用于生物制药生产的细胞系模型的准确性.
科学领域:
- 生物技术是生物技术.
- 代谢工程是代谢工程.
- 系统生物学 系统生物学
背景情况:
- 哺乳动物细胞培养对于生物制药生产至关重要.
- 机械代谢模型捕捉了细胞复杂性,但往往忽视了酶动态.
- 酶丰度和活性的时间变化显著影响了代谢建模.
研究的目的:
- 为机械代谢模型开发一个框架,整合酶活性和转录动态的生长信号控制.
- 将这一框架应用于用于生物制造的中国仓鼠卵巢 (CHO) 细胞系.
- 提高细胞系模型的准确性和预测能力.
主要方法:
- 开发了一种机械代谢模型的框架,其中包含了酶活性和转录动态.
- 将框架应用于三种中国仓鼠卵巢 (CHO) 细胞系,使用养批量培养数据和时间序列转录档案.
- 实施多细胞系 (MCL) 学习方法进行参数估计,结合来自不同细胞系的数据.
主要成果:
- 在代谢模型中证明了生长信号和转录变异性在代谢模型中的重要作用.
- 展示了MCL方法的有效性,用于构建精确的细胞系模型,但数据有限.
- 在预测不同CHO细胞系的不同代谢行为方面取得了高准确性.
结论:
- 开发的框架通过整合酶动态和转录配置文件,准确地模拟细胞代谢.
- 该MCL学习方法提高模型的准确性,并在数据有限时是有益的.
- 这些模型可以加速生物过程和细胞系的发展,用于蛋白质治疗制造.
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