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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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Andre Norfleet
BioE PhD Proposal Presentation
Date: June 12th, 2020
Time: 1:00 PM
Location: https://bluejeans.com/291598154
Committee Members:
- Dr. Melissa Kemp (Advisor, BME, Georgia Tech)
- Dr. Manu Platt (BME, Georgia Tech)
- Dr. Eberhard Voit (BME, Georgia Tech)
- Dr. Ravi Kane (ChBE, Georgia Tech)
- Dr. Craig Forest (ME, Georgia Tech)
- Dr. Sung-Jin Park (BME, Georgia Tech)
Metabolic and Bioelectric Crosstalk in Directed Differentiation and Spatial Patterning of iPSC-derived Cardiomyocytes
Bioelectric cell physiology and signaling have recently received much attention for their exerted control over multicellular regenerative and developmental patterning outcomes. More specifically, resting membrane potentials, ionic currents, and long-range electric fields define bioelectric signals that dictate non-excitable cell developmental patterning. Understanding bioelectric system dynamics and their relation to downstream developmental pattern trajectories would provide engineers the ability to design and manipulate multicellular systems to specified phenotypes. Efforts to understand these mechanisms are complicated because the Vmem-determining ion channels and gap junctions are also modulated by Vmem, resulting in complex system feedback. Development of a bioelectric computational simulation platform enables a realistic bottom-up bioelectric modeling approach in which the coordinated dynamics of passive and active ion fluxes dictate spatiotemporal bioelectric patterning dynamics. The overall objective of this project is to determine the role of ionic fluxes in the creation and maintenance spatiotemporal bioelectric patterns present in iPSC multicellular clusters. The central hypothesis is that metabolism influences bioelectrical patterning through pH, ATP, and cell cycle effects that direct subsequent multicellular properties during iPSC-cardiomyocyte differentiation. Ultimately, the objective of this project as executed in three aims will relate bioelectric signaling cues, cell metabolism, and intercellular communication to downstream spatiotemporal phenotypic trajectories and will further provide a foundational basis for assisted control interventions of spatiotemporal bioelectric dynamic outcomes and downstream phenotypic trajectories.
