PhD Proposal Presentation by James Wade

Event Details
  • Date/Time:
    • Thursday September 4, 2014
      9:00 am - 11:00 am
  • Location: IBB 1128
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Summaries

Summary Sentence: Computational Modeling and Analysis of Single-cell Signaling Dynamics in Heterogeneous Cell Populations

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James Wade
PhD Proposal Presentation
Date: Thursday, September 4th, 2014
Time: 9 AM
Location: IBB 1128

Advisors:
Eberhard Voit, PhD (GT-BME)
Barbara Boyan, PhD (Virginia Commonwealth University)

Committee:
Bernd Bodenmiller, PhD (University of Zurich)
Melissa Kemp, PhD (GT-BME)
John McDonald, PhD (GT-Biology)
Peng Qiu, PhD (GT-BME)

Title:
Computational Modeling and Analysis of Single-cell Signaling Dynamics in Heterogeneous Cell Populations

Abstract:
Tumors and tissues represent heterogeneous cell populations. This heterogeneity may be due to non-genetic variability in cell state, which is determined by factors such as stochastic gene expres- sion or local differences in microenvironment. The cell state affects cellular signaling responses to a given set of inputs. Intracellularly, signal transduction is determined by a complex network of biochemical reactions that can exhibit complex nonlinear dynamics. The goal of this work is to computationally analyze signaling dynamics at the single-cell level in heterogeneous cell popu- lations. Generally, experimental studies of single-cell signaling are limited by the choice between experimental methods that can either measure the state of a few molecules in the same cells con- tinuously, or time courses of many molecules from separate cells. We propose to bridge this gap with a novel computational methodology that allows us to model and analyze single-cell signaling dynamics. The innovation of this method will be its ability to predict single-cell trajectories based on time course experiments of different cells. We will apply our method to the investigation of intracellular signaling in heterogeneous B cell and breast cancer cell populations, using data from mass cytometry (CyTOF) experiments. The computational analysis is expected to have the capac- ity of predicting novel therapeutic targets as a function of the initial cell state. These predictive results will be tested with targeted CyTOF experiments. We believe this work will significantly increase our understanding of signaling at the single-cell level.

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Graduate Studies

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Keywords
BME, Graduate Student, PhD, thesis
Status
  • Created By: Danielle Ramirez
  • Workflow Status: Published
  • Created On: Aug 26, 2014 - 10:05am
  • Last Updated: Oct 7, 2016 - 10:08pm