Advanced Search

Origins of cell-to-cell variability in apoptosis

Research and Teaching Output of the MIT Community

Show simple item record

dc.contributor.advisor Peter K. Sorger. en_US Spencer, Sabrina Leigh en_US
dc.contributor.other Massachusetts Institute of Technology. Computational and Systems Biology Program en_US 2010-05-27T19:48:45Z 2010-05-27T19:48:45Z 2009 en_US 2009 en_US
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2009. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 127-142). en_US
dc.description.abstract Diversity within a population of organisms is typically ascribed to genetic differences. However, even members of a genetically identical group of cells or organisms in identical environments can exhibit variability in state and phenotype. One striking example of such heterogeneity is revealed when a genetically identical population of human cells is exposed to saturating doses of a death-inducing drug called TRAIL - many cells in the population will undergo apoptosis, a form of controlled cell death, but a fraction of cells always survives the treatment. The goal of this thesis was to understand the origins of variability in both the timing and the probability of death in TRAIL-induced apoptosis. To this end, both experimental and computational methods were implemented. Experiments examining the response of sister cells to TRAIL provided strong evidence that variability in initial conditions played a key role, and ruled out genetic, stochastic, and cell cycle effects as possible causes of heterogeneity in response. A detailed analysis of the relative contributions of three segments of the TRAIL pathway revealed that the majority of the variability in time-to-death arose upstream of mitochondrial outer membrane permeabilization (MOMP), with little contribution from downstream reactions. More specifically, the rate of cleavage of initiator caspase substrates was highly predictive of a cell's death time. However, to determine whether (as opposed to when) a cell will die, variation in the MOMP threshold became critical. en_US
dc.description.abstract (cont.) This dependency was indicated by observation of the height of the MOMP threshold in surviving and dying cells and by modulation of this threshold via overexpression of anti-apoptotic regulators of MOMP. Simulations of cell-to-cell variability in TRAIL-induced apoptosis confirmed that the endogenous variability in apoptotic regulators was sufficient to produce the observed variability in death time. However, knowledge of the concentration of individual proteins did not allow prediction of death time because variation in other proteins masked the underlying trends. The ability to simulate heterogeneity in cellular response also led to the development of novel, biologically intuitive methods of sensitivity analysis, which revealed that sensitivities shift depending on whether knowledge of covariance in initial conditions is included. The ability to predict sensitivity and resistance of tumors to TRAIL would be clinically valuable, as TRAIL is currently in clinical trials as an anti-cancer therapy. The results described here represent progress toward understanding the "fractional killing" of tumor cells following exposure to chemotherapy, and for understanding variability in mammalian signaling pathways in general. en_US
dc.description.statementofresponsibility by Sabrina Leigh Spencer. en_US
dc.format.extent 142 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri en_US
dc.subject Computational and Systems Biology Program en_US
dc.title Origins of cell-to-cell variability in apoptosis en_US
dc.type Thesis en_US Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Computational and Systems Biology Program en_US
dc.identifier.oclc 549389244 en_US

Files in this item

Name Size Format Description
549389244-MIT.pdf 43.39Mb PDF Full printable version

This item appears in the following Collection(s)

Show simple item record