Single cell decisions in endothelial population in the context of inflammatory angiogenesis
Name
795196988-MIT.pdf
Description
Full printable version
Size
28.98 MB
Format
Adobe PDF
Checksum (MD5)
efccc5880d8c023f3bdf8a06281041a6
Author(s)
Rimchala, Tharathorn
Advisor(s)
Douglas A. Lauffenburger.
Date Issued
2012
Publisher
Massachusetts Institute of Technology
Abstract
Normalizing angiogenesis is a promising strategy for treatments of cancer and several disorders plagued by misregulated blood supplies. To address the daunting complexity of angiogenesis arising from multiple phenotypic behaviors governed by multiple stimuli, computational approaches have been developed to predict sprouting angiogenic outcomes. In recent years, the agent based model, in which individual cells are modeled as autonomous decision making entities, has become an important tool for simulating complex phenomena including angiogenesis. The reliability of these models depends on model validation by quantitative experimental characterization of the cellular (agent) behaviors which so far has been lacking. To this end, I develop an experimental and computational method to semi-automatically estimate parameters describing the single-cell decision in the agent based model based on flow cytometry aggregate headcount data and single cell microscopy which yields full panel single cell trajectories of individual endothelial cells. Applying thees method to the single cell decision data, I propose two conceptual models to account for the different state transition patterns and how they are modulated in the presence of opposing inflammatory cytokines. The observed unique state transition patterns in the angiogenic endothelial cell population are consistent with one of these descriptions, the diverse population model (DPM). The DPM interpretation offers an alternative view from the traditional paradigm of cell population heterogeneity. This understanding is important in designing appropriate therapeutic agents that take effect at the cellular level to meet a tissue level therapeutic goal.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 161-171).
Subjects
Biological Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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