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Updated: Sep 15, 2025

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轮子转动:CHO细胞建模进入数字生物制造时代:CHO代谢建模

Sandeep Ranpura¹, Vishwanathgouda Maralingannavar¹, Alexandra-Gabriela Gheorghe¹

¹Lonza Biologics plc, Slough, United Kingdom.

Computational and structural biotechnology journal

|July 18, 2025

概括

先进的建模和机器学习正在优化中国仓鼠卵巢 (CHO) 细胞生物过程. 这些工具通过实时过程控制和预测来提高药物制造效率并降低成本.

科学领域:

生物技术是生物技术.
生物工艺工程生物工艺工程
计算生物学计算生物学

背景情况:

CHO细胞培养的进展依赖于了解环境对新陈代谢和生产力的影响.
在模型对于预测过程参数效应至关重要.
工业4.0推动生物制药开发中的数字化和智能制造.

研究的目的:

审查CHO细胞代谢的机制建模,包括糖化和分泌途径.
讨论多omics数据的整合,以更深入地了解细胞内过程.
探索机器学习和人工智能在生物制药开发中的应用,以优化和控制.

主要方法:

机械模型 (动力学,基于约束的) 的CHO中心代谢.
整合多omics数据 (基因组学,转录组学,蛋白质组学,代谢组学).
数据驱动分析,自学系统,数字双胞胎和人工智能的应用.

主要成果:

结合omics数据的机械模型为产品质量和路径动态提供了洞察力.
数字化和机器学习促进了实时流程控制和预测能力.
混合模型对高效和成本效益的药物开发有希望.

结论:

机械和数据驱动的建模方法对于推进CHO生物过程至关重要.
机器学习和人工智能加速了生物制剂的开发和制造.
优化生物工艺导致更高效,更实惠的药物生产.

关键词:

生物制造生物制造生物制造在CHO代谢中.混合动力模型混合动力模型机器学习机器学习机械模型建模机械模型俄米克斯 (Omics) 是一个电子游戏.

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