Title:
Analyzing multicellular interactions: A hybrid computational and biological pattern recognition approach

dc.contributor.advisor Kemp, Melissa L.
dc.contributor.advisor McDevitt, Todd C.
dc.contributor.author White, Douglas
dc.contributor.committeeMember Voit, Eberhard O.
dc.contributor.committeeMember Stice, Steve L.
dc.contributor.committeeMember Weiss, Ron
dc.contributor.committeeMember Platt, Manu O.
dc.contributor.department Biomedical Engineering (Joint GT/Emory Department)
dc.date.accessioned 2016-05-27T13:10:08Z
dc.date.available 2016-05-27T13:10:08Z
dc.date.created 2015-05
dc.date.issued 2015-04-07
dc.date.submitted May 2015
dc.date.updated 2016-05-27T13:10:08Z
dc.description.abstract Pluripotent embryonic stem cells (ESCs) can differentiate into all somatic cell types, making them a useful platform for studying a variety of cellular phenomenon. Furthermore, ESCs can be induced to form aggregates called embryoid bodies (EBs) which recapitulate the dynamics of development and morphogenesis. However, many different factors such as gradients of soluble morphogens, direct cell-to-cell signaling, and cell-matrix interactions have all been implicated in directing ESC differentiation. Though the effects of individual factors have often been investigated independently, the inherent difficulty in assaying combinatorial effects has made it difficult to ascertain the concerted effects of different environmental parameters, particularly due to the spatial and temporal dynamics associated with such cues. Dynamic computational models of ESC differentiation can provide powerful insight into how different cues function in combination both spatially and temporally. By combining particle based diffusion models, cellular agent based approaches, and physical models of morphogenesis, a multi-scale, rules-based modeling framework can provide insight into how each component contributes to differentiation. I propose to investigate the complex regulatory cues which govern complex morphogenic behavior in 3D ESC systems via a computational rules based modeling approach. The objective of this study is to examine how spatial patterns of differentiation by ESCs arise as a function of the microenvironment. The central hypothesis is that spatial control of soluble morphogens and cell-cell signaling will allow enhanced control over the patterns and efficiency of stem cell differentiation in embryoid bodies.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/54876
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Computational modeling
dc.subject Machine learning
dc.subject Embryonic stem cells
dc.subject Image informatics
dc.subject Spatial patterns
dc.subject 3D cellular aggregates
dc.title Analyzing multicellular interactions: A hybrid computational and biological pattern recognition approach
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Kemp, Melissa L.
local.contributor.corporatename Wallace H. Coulter Department of Biomedical Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 829416a8-1bef-4485-ba85-c0d21b797771
relation.isOrgUnitOfPublication da59be3c-3d0a-41da-91b9-ebe2ecc83b66
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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