In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement.

在生物技术中，高通量技术的出现对大型数据集的解释提出了挑战。从大型数据集中识别影响过程和产品属性的有意义结果的一种方法是使用系统生物学工具，例如代谢模型。但是，由于我们的知识和技术局限性的不足，这些工具仍未在工业环境中完全用于此目的。在本文中，在三个研究领域（监视，网络科学和混合建模）中重点介绍了限制这些工具的常规实现的关键方面。这些领域的进展可能会扩大系统生物学在生物制药行业中的应用现状，以应对生物工艺开发和改进中的现有挑战。