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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Advisor: Melissa L. Kemp, PhD (BME, Georgia Tech & Emory University)
Committee Members:
Wilbur A. Lam, MD, PhD (BME/Pediatrics, Georgia Tech & Emory University)
Manu O. Platt, PhD (BME, Georgia Tech & Emory University)
David K. Wood, PhD (BME, University of Minnesota)
Levi B. Wood, PhD (ME, Georgia Tech)
Informing Precision Medicine Through Data-driven Modeling of Patient-Specific Therapeutic Responses in Microfluidic-based Assays
Precision medicine has the potential to improve patient outcomes through customized clinical decisions for many diseases but is reliant on availability of robust biomarkers and assays for biomarker detection that can accurately quantify the disease state. Microfluidic devices are powerful diagnostic and research tools for functional testing of patient samples; these devices are increasingly sophisticated by incorporating physiological features such as the cellular environment, thus better recapitulating in vivo behavior. As these platforms continue to incorporate more features, analysis and interpretation of patterns within these multi-factorial datasets becomes challenging. For example, high-speed imaging technologies allows for the capture of high throughput quantitative data that incorporate dynamic signals and responses within a single experiment. Without interpretable models of these complex datasets, experimental observations are difficult to translate into clinically actionable insights for precision medicine. New statistical and computational methods are needed to extract the maximal amount of information from the analysis of microfluidics-generated data and overcome the challenges of modeling biological data. The overall objective of this thesis is to leverage computational and mathematical approaches to develop robust predictive models of patient sample response to combinatorial therapies assayed in microfluidic devices. I will validate my approach using microfluidics-generated datasets from application to two hematologic diseases: multi-drug resistance profiling in leukemia, and oxygen-dependent rheological biomarkers in sickle cell disease vaso-occlusion. The frameworks developed here will result in models that can extract important features from multi-factorial experiments, optimize discovery of synergistic interactions, and provide personalized recommendations for therapy.
