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

> **NIH NIH R41** · REDBUD LABS, INC. · 2020 · $296,954

## Abstract

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.

## Key facts

- **NIH application ID:** 9909760
- **Project number:** 1R41GM136084-01
- **Recipient organization:** REDBUD LABS, INC.
- **Principal Investigator:** Cass T Miller
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $296,954
- **Award type:** 1
- **Project period:** 2020-09-10 → 2022-06-20

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9909760

## Citation

> US National Institutes of Health, RePORTER application 9909760, Enhanced MDx: a computational model to optimize pre-analytical pathogen isolation from whole blood. (1R41GM136084-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9909760. Licensed CC0.

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