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

摘要 微尺度模拟已经被应用于许多复杂的微流体系统和生物系统。 虽然现有的方法在可解决的问题的规模和范围上是有限的。 热力学约束平均理论（TCAT）是一种已建立的方法，可用于 制定与微观物理学和热力学一致的定制宏观模型。 TCAT建模框架已经开发、评估和验证，用于广泛的应用 涉及流体和固相，然而，模拟尚未实现可移动的固相， 复杂流体这种组合代表了微流体系统的快速增长部分，特别是 那些针对床旁诊断（POC Dx），因为微流体和芯片实验室设备是关键驱动因素 的市场增长。 在第一阶段的研究中，我们提出了一种计算机模拟的方法来帮助微流体模块的设计， 从样品中分离和浓缩靶以显著提高测定灵敏度。该项目结合了 Redbud Labs的可致动后处理技术可通过建模实现快速病原体分离和浓缩 查佩尔山的北卡罗来纳州大学的米勒和格里菲斯实验室的专业知识。 在目标1中，我们将开发描述牛顿和非牛顿流体流动的计算模型， 在无流动条件下，在包含致动柱的微流体腔室中的物种。在目标2中，我们将扩展 微流控系统的模型与灌注，反应，和质量传递到致动柱，包括 粒子输运在目标3中，我们将预测具有设计特征的微流体细胞的行为， 在上述目标中进行了测试。将比较模拟结果和模型输出 对比实验数据。完成的计算模型将推动优化和发展 创新的微流控系统，适用于广泛的潜在应用。

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.