Designing Diagnostic and Prognostic Platforms for Analysis and Probing Disease Emergence

Rohani, Ali, Electrical Engineering - School of Engineering and Applied Science, University of Virginia
Swami, Nathan, Department of Electrical and Computer Engineering, University of Virginia

The emerging era of personalized medicine relies on medical decisions, practices, and products being tailored to the individual patient. Point-of-care systems, at the heart of this model, play two important roles. First, they are required for identifying subjects for optimal therapies based on their genetic make-up and epigenetic profile. Second, they will be used for assessing the progression of such therapies. Central to this vision is designing systems that, with minimal user-intervention, can transduce complex signals from biosystems in complement with clinical information to inform medical decision within point-of-care settings. To reach our ultimate goal of developing point-of-care systems and realizing personalized medicine, we are taking a multistep systems-level approach towards understanding cellular processes and biomolecular profiles, to quantify disease states and external interventions.
The first step is to generate data. In this step we develop necessary tools to enable reliable and repeatable set of experiments, to facilitate sensitive characterization of biosystems of interest. To this aim, we have developed AC electrokinetic methods within microfluidic systems to translate the bioparticles based on their unique electrophysiology. Innovative device structures and signaling schemes are essential for sensitive characterization of biosystems and deciphering the underlying mechanism responsible for emergent behavior of these systems. To accomplish our goals in this step, we have designed relevant device structures or signaling schemes to generate data. In the first chapter, we presented a nano-slit structure to create conductivity gradients. These patterned conductivity gradients are used to enhance dielectrophoretic trapping of nanometer sized proteins, antibodies and antigens to enable and enhance discovery of rare cancer biomarkers in high conductivity physiological media. Similarly in Chapter Two we employ AC dielectrophoresis inside an insulator based microfluidic structure to characterize and separate Human Embryonic Kidney (HEK) and Mouse Embryonic Fibroblast (MEF) cells, based on their unique mitochondrial morphology and subsequent dissimilar electrophysiology.
The second step is turning data into information. To this end, we develop tools to monitor bioparticles and perform accurate measurements on them. These measurements range from tracking bioparticles to quantifying their morphometric measures. Finding proper figures to measure and perform accurate measurements is the key factor for sensitive characterization of biosystems. To achieve this goal, in chapter one, we develop a sensitive methodology to quantify pre-concentration and depletion of different biomarkers in micro/nano fluidic systems. The unique characteristics of this methodology are: highly reduced measured noise and geometry free analysis capabilities. In Chapter Two, we developed MyMiA; an image analysis software to quantify morphometric properties of mitochondrial networks.
The third step is to turn information into insight. In this step, we use our gained information in conjunction with the underlying mechanistic knowledge to predict cellular/molecular behaviors leading to disease states and drug responses. To this end, we benefit from modeling, computation and simulations to better understand the systems of interest. To achieve this goal, in Chapter One, mathematical modeling of protein dielectric properties along with intense numerical simulations enabled us to design, enhance and optimize a biomarker-sensing system, and in chapter two, by using mathematical modeling we found the correlation between mitochondrial network properties (connectedness) and cells electrophysiological response, which enabled us to speared cells based on the level of connectivity in their mitochondrial network in a completely label free manner. Also by using morphometric measures from MyMiA and applying supervised learning algorithms, we showed cell line and certain proteomic activities in cells could be predicted based on their mitochondrial morphology which is of great importance for predicting patient outcomes and also developing new studies on cancer cells.

PHD (Doctor of Philosophy)
Cancer, Nano fluidics, Electrokinetics, Machine learning, Data Science, Early detection of cancer, Personalized medicine, MyMiA, Mitochondria
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