Enhanced MDx: a computational model to optimize pre-analytical pathogen isolation from whole blood.

摘要 微尺度模拟已经应用于许多复杂的微流体系统和生物 但现有方法在可解决问题的规模和范围方面受到限制。 热力学约束平均理论(TCAT)是一种成熟的方法，可用于 制定与微观物理和热力学相一致的定制宏观模型。 TCAT建模框架已经针对广泛的应用程序进行了开发、评估和验证 涉及流体和固体相，但尚未实现对可移动固体相和 复杂的流体。这一组合代表了微流控系统的一个快速增长的细分市场，尤其是 以关怀点诊断(POC Dx)为目标的产品，因为微流体和芯片实验室设备是关键驱动因素 市场增长的影响。 在这个第一阶段的研究中，我们提出了一种硅内方法来辅助微流控模块的设计，以快速 从样本中分离和浓缩靶标，以显著提高分析灵敏度。这个项目结合了 Redbud Labs的可执行POST技术可实现病原体的快速分离和浓缩 北卡罗来纳大学教堂山分校米勒和格里菲斯实验室的专业知识。 在目标1中，我们将开发一个描述牛顿和非牛顿流体流动的计算模型，并 在无流动条件下，含有致动柱的微流体室中的物种。在目标2中，我们将扩展 微流控系统的模型包括到执行柱的灌流、反应和传质，包括 粒子传输。在目标3中，我们将预测具有设计特征的微流控电池的行为 之前在上述目标中进行了测试。将模拟结果与模型输出进行比较 与实验数据对比。完成的计算模型将为优化和发展 创新的微流控系统，具有广泛的潜在应用。

ABSTRACT Microscale simulations have been applied to a number of complex microfluidic systems and biological applications, but existing methods are limited in the scale and scope of problems that are addressable. Thermodynamically constrained averaging theory (TCAT) is an established approach that can be used to formulate customized macroscale models that are consistent with microscale physics and thermodynamics. TCAT modeling frameworks have been developed, evaluated, and validated for a wide range of applications involving fluid and solid phases, however simulations have yet to be realized for movable solid phases and complex fluids. This combination represents a rapidly growing segment of microfluidic systems, especially those targeted at Point of Care Diagnostics (POC Dx), as microfluidics and lab-on-chip devices are key drivers of market growth. In this Phase I study, we propose an in-silico approach to aid the design of microfluidic modules to rapidly isolate and concentrate targets from specimens to dramatically improve assay sensitivity. This project combines Redbud Labs’ actuatable post technology enabling rapid pathogen isolation and concentration with the modeling expertise of the Miller and Griffith Labs at the University of North Carolina at Chapel Hill. In Aim 1, we will develop a computational model describing Newtonian and non-Newtonian fluid flow and species in a microfluidic chamber containing actuating posts under no-flow conditions. In Aim 2, we will extend the model to microfluidic systems with perfusion, reactions, and mass transfer to the actuating posts, including particle transport. In Aim 3, we will predict the behavior of microfluidic cells with design characteristics not previously tested in the above-mentioned aims. Results of the simulations and model outputs will be compared against experimental data. The completed computational model will fuel the optimization and development of innovative microfluidic systems for a wide range of potential applications.