Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

IF 3.7 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Yusmel González-Hernández, Patrick Perré
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引用次数: 0

Abstract

This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.

Abstract Image

生物过程机理建模所需的基础模块:基于 CHO 细胞生产蛋白质的重要综述
本文回顾了开发用作生产操作工具的机理模型所需的关键构件。中国仓鼠卵巢(CHO)细胞是制药业生产抗体最广泛使用的宿主之一,本文以该细胞为案例进行研究。CHO 细胞的新陈代谢有两个主要阶段:指数增长期和静止期,前者可产生大量蛋白质。这一过程具有适当的复杂性,因此需要概述建模策略。本文分为四个主要步骤:(1) CHO 系统和数据收集;(2) 代谢分析;(3) 数学模型的制定;最后,(4) 数值求解、校准和验证。整个方法可以建立目标变量的预测模型。根据文献,目前建模的主要挑战之一在于理解和预测自发的新陈代谢转变。触发新陈代谢转变的可能因素包括乳酸和二氧化碳的浓度。我们认为,铵也是一种抑制产物,应进一步研究。最后,本文介绍了混合建模这一新兴领域的预期进展,它结合了机理建模和机器学习的优点,是一项引人入胜的突破。请注意,本文讨论的建模策略是一个通用框架,可应用于任何生物过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Metabolic Engineering Communications
Metabolic Engineering Communications Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
13.30
自引率
1.90%
发文量
22
审稿时长
18 weeks
期刊介绍: Metabolic Engineering Communications, a companion title to Metabolic Engineering (MBE), is devoted to publishing original research in the areas of metabolic engineering, synthetic biology, computational biology and systems biology for problems related to metabolism and the engineering of metabolism for the production of fuels, chemicals, and pharmaceuticals. The journal will carry articles on the design, construction, and analysis of biological systems ranging from pathway components to biological complexes and genomes (including genomic, analytical and bioinformatics methods) in suitable host cells to allow them to produce novel compounds of industrial and medical interest. Demonstrations of regulatory designs and synthetic circuits that alter the performance of biochemical pathways and cellular processes will also be presented. Metabolic Engineering Communications complements MBE by publishing articles that are either shorter than those published in the full journal, or which describe key elements of larger metabolic engineering efforts.
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