Article: Smith, JP; Kirby, BJ; (2015) “A Transfer Function Approach for Predicting Rare Cell Capture Microdevice Performance”, Biomedical Microdevices, 17 (3)
Abstract: Rare cells have the potential to improve our understanding of biological systems and the treatment of a variety of diseases; each of those applications requires a different balance of throughput, capture efficiency, and sample purity. Those challenges, coupled with the limited availability of patient samples and the costs of repeated design iterations, motivate the need for a robust set of engineering tools to optimize application-specific geometries. Here, we present a transfer function approach for predicting rare cell capture in microfluidic obstacle arrays. Existing computational fluid dynamics (CFD) tools are limited to simulating a subset of these arrays, owing to computational costs; a transfer function leverages the deterministic nature of cell transport in these arrays, extending limited CFD simulations into larger, more complicated geometries. We show that the transfer function approximation matches a full CFD simulation within 1.34 %, at a 74-fold reduction in computational cost. Taking advantage of these computational savings, we apply the transfer function simulations to simulate reversing array geometries that generate a “notch filter” effect, reducing the collision frequency of cells outside of a specified diameter range. We adapt the transfer function to study the effect of off-design boundary conditions (such as a clogged inlet in a microdevice) on overall performance. Finally, we have validated the transfer function’s predictions for lateral displacement within the array using particle tracking and polystyrene beads in a microdevice.
Funding Acknowledgement: Cornell Center on the Microenvironment & Metastasis through from the National Cancer Institute Physical Sciences Oncology Center (NCI PS-OC) [U54CA143876]
Funding Text: This work was supported by the Cornell Center on the Microenvironment & Metastasis through Award Number U54CA143876 from the National Cancer Institute Physical Sciences Oncology Center (NCI PS-OC).