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

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.NARRATIVE Computational models and simulations are invaluable tools for improving microfluidic device development to keep up with an increasing demand for better (faster, more sensitive) technology. In this Phase I study, we propose an in-silico approach to aid the design of microfluidic modules to rapidly isolate, concentrate, and purify targets for diagnostic testing, thus dramatically improving overall assay sensitivity. With this computational model, we will extend current capabilities to include target sizes spanning three orders of magnitude, from single molecules to single cells.