Understanding the impact of shear stresses on different cell lines in the Allegro STR system

该项目的第一部分旨在量化Allegro系统在不同运行条件下的最大和平均雷诺应力。这可以通过（i）设计能够再现大规模反应器的几何形状、环境和流动动力学特性的精确的按比例缩小的装置和/或（ii）调整Allegro反应器以便通过实验流动动力学技术光学地可访问，从而直接测量系统中的应力来实现。第二步将涉及选择三种代表性细胞系，并使用现有或新的USD工具，以更好地了解细胞对Allegro STR中先前测量的应力范围的反应。将在细胞培养步骤的不同阶段收集分析信息（代谢谱、细胞计数、粒度分布、扫描电子显微镜等），以量化细胞损伤的程度。结合两个阶段获得的实验结果，除了大规模和潜在瓶颈所面临的挑战之外，还将提供对生物反应器几何形状、操作条件和所产生的应力之间的相互作用的透彻和完整的了解。实验工作可以与建模方法相结合，以更好地理解气体流速/气体分布模式之间的关系，其在实验上难以测量以及在不同操作条件下的电池损伤。该项目的这一部分将产生预测相关性，能够为未来的缩小模型设计提供信息，并支持验证缩放相关性和确定大规模的最佳操作条件。

The first part of the project will aim to quantify maximum and average Reynolds stresses in the Allegro system at different operating conditions. This can be achieved by (i) designing an accurate scale down device able to reproduce the geometry, environment and flow dynamics characteristics of the large scale reactor and/or (ii) adapting the Allegro reactor in order to be optically accessible by experimental flow dynamics techniques and thus directly measuring stresses in the system. A second step will involve the selection of three representative cell lines and the use of existing or new USD tools to better understand cell response to the range of stresses previously measured in the Allegro STR. Analytical information will be collected at different stages of the cell culture step (metabolic profiles, cell counting, particle size distribution, Scanning Electron Microscopy among others) to quantify the extent of cell damage. The combination of the experimental results obtained in the two stages will provide a thorough and complete insight of the interaction between the bioreactor geometry, the operating conditions and the resulting stresses, in addition to the challenges faced at large scale and potential bottlenecks.The experimental work could be combined with a modelling approach to provide better understanding of the relationship between gas flow rate/gas distribution patterns, which are experimentally challenging to measure, and cell damage under different operating conditions. This part of the project will result in predictive correlations, able to inform future scale-down model design and support the validation of scaling correlations and identification of optimal operating conditions at the large scale.