Ph.D. Proposal by Linda Kippner

Event Details
  • Date/Time:
    • Thursday October 30, 2014
      2:00 pm - 4:00 pm
  • Location: MSE Room G021, Georgia Tech
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Summaries

Summary Sentence: Investigating heterogeneity in immune cell populations using single cell gene expression, computational modeling, and microfluidics.

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Linda Kippner
Ph.D. Proposal Presentation
Date: Thursday, October 30, 2014
Time: 2:00 PM
Location: MSE Room G021, Georgia Tech

Committee members:
Advisor: Dr. Melissa Kemp, BME, Georgia Tech
Dr. Greg Gibson, School of Biology, Georgia Tech
Dr. Manu Platt, BME, Georgia Tech
Dr. Peng Qiu, BME, Georgia Tech
Dr. Rabindra Tirouvanziam, School of Medicine, Emory University

Title: Investigating heterogeneity in immune cell populations using single cell gene expression, computational modeling, and microfluidics.

Abstract:
It is becoming increasingly apparent that heterogeneity within cell populations is an important feature of with functional consequences. Such data is easily masked by standard bulk sample techniques for data acquisition and analysis. While our knowledge of such variability has been previously hindered by technical limitations, the emergence of single cell techniques now makes it possible to investigate at a much higher resolution and throughput. The overall objective of this thesis is to apply a single cell approaches to the study of functional heterogeneity within immune cell populations. The innovation of this lies in the use of novel methods for single cell gene expression data acquisition with a systematic evaluation of data analysis methods in order to determine their suitability for this type of data. This addresses the current lack of consensus regarding the appropriate methods of single cell gene expression data processing. In addition, by the combined use of computational modeling and novel microfluidic devices capable of applying dynamic stimulus, we aim to extract information regarding single cell response variability within a population. The significance of such an approach is that it incorporates the effects of dynamic input into a system of receptor ligand interaction, such as those of cytokine signaling within the immune system. Through the combination of emerging techniques for high throughput single cell analysis, computational modeling, and systematic evaluation of single cell data analysis methods, we hope to gain new insight into cell-cell variability within immune cell populations, characterized at multiple levels, from gene expression to cell dynamics.

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Keywords
graduate students, Phd proposal; thesis
Status
  • Created By: Danielle Ramirez
  • Workflow Status: Published
  • Created On: Oct 17, 2014 - 10:16am
  • Last Updated: Oct 7, 2016 - 10:09pm