Bioprocess scale-up is a critical step in process development. However, loss of production performance upon scaling-up, including reduced titer, yield, or productivity, has often been observed, hindering the commercialization of biotech innovations. Recent developments in scale-down studies assisted by computational fluid dynamics (CFD) and powerful stimulus–response metabolic models afford better process prediction and evaluation, enabling faster scale-up with minimal losses. In the future, an ideal bioprocess design would be guided by an in silico model that integrates cellular physiology (spatiotemporal multiscale cellular models) and fluid dynamics (CFD models). Nonetheless, there are challenges associated with both establishing predictive metabolic models and CFD coupling. By highlighting these and providing possible solutions here, we aim to advance the development of a computational framework to accelerate bioprocess scale-up.

生物工艺放大是工艺开发中的关键步骤。然而，经常观察到规模扩大时生产性能的损失，包括滴度，产量或生产率的降低，这阻碍了生物技术创新的商业化。缩小研究的最新进展借助计算流体动力学（CFD）和强大的刺激响应代谢模型提供了更好的过程预测和评估，从而能够以最小的损失实现更快的放大。将来，理想的生物过程设计将以计算机模拟为指导集成了细胞生理学（时空多尺度细胞模型）和流体动力学（CFD模型）的模型。尽管如此，建立预测性代谢模型和CFD耦合都存在挑战。通过重点介绍这些内容并在此处提供可能的解决方案，我们旨在推进计算框架的开发，以加速生物工艺的规模化。