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dc.contributor.advisorVoit, Eberhard O.
dc.contributor.authorWade, James Donald
dc.date.accessioned2020-05-20T16:55:42Z
dc.date.available2020-05-20T16:55:42Z
dc.date.created2019-05
dc.date.issued2019-03-01
dc.date.submittedMay 2019
dc.identifier.urihttp://hdl.handle.net/1853/62648
dc.description.abstractCell signaling pathways are complex biochemical systems at the core of cellular information processing. The dynamics of these signaling systems in response to internal and extracellular cues plays a critical role for proper cell functioning. While we have learned much about signaling at the cell population level, no two cells are the same, and cell-to-cell variability can have complex and important consequences for signaling in both individual cells and the cell population as a whole. In many contexts, cells perform essentially identical functions despite their differences, whereas in other contexts, especially in cancer, cell-cell differences in state propagate to differences in function. The overall goal of this dissertation was the creation of mathematical and computational tools for the study of cell-to-cell variation in signaling and to use these tools to increase our understanding of when single cell differences do, or do not, make a meaningful difference. To address this goal we designed new methods of single-cell analysis, including a computational framework termed single-cell ordinary differential equation modeling (SCODEM) that overcomes the prior experimental trade-off between continuous and multiplexed single-cell measurements of signaling. We tested SCODEM against increasingly demanding datasets, which were all represented in a satisfactory fashion. After the initial analysis of cell-to-cell variability, we analyzed targeted inhibition, protein overexpression and an epithelial-mesenchymal transition. Throughout this process, we provided illustrative examples of how our modeling framework may be used to identify operating principles and limits of signaling systems, which is a first step toward proposing novel therapeutic targets. The work presented here provides new tools for analyzing cellular heterogeneity and increases our understanding of how differences in cell state effect function by showing intracellular signaling is primarily deterministic at the single cell level. The application of these tools to the dramatic phenotype shift during an epithelial-mesenchymal transition in murine breast cancer cells confirmed that stochasticity plays a much smaller role than had been assumed and that cells modulate signaling without the need of rewiring their signaling network.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectComputational biology
dc.subjectComputational modeling
dc.subjectSystems biology
dc.subjectSingle cell
dc.subjectMass cytometry
dc.subjectSignaling
dc.subjectDynamics
dc.subjectInference
dc.subjectMultiplexed
dc.subjectMultivariate
dc.subjectSnapshot
dc.subjectTrajectory
dc.titleComputational modeling and analysis of single-cell signaling dynamics in heterogeneous cell populations
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentBiomedical Engineering (Joint GT/Emory Department)
thesis.degree.levelDoctoral
dc.contributor.committeeMemberBodenmiller, Bernd
dc.contributor.committeeMemberBoyan, Barbara D.
dc.contributor.committeeMemberKemp, Melissa L.
dc.contributor.committeeMemberMcDonald, John F.
dc.contributor.committeeMemberQiu, Peng
dc.date.updated2020-05-20T16:55:42Z


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