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Bayesian network models of biological signaling pathways

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dc.contributor.advisor Douglas A. Lauffenburger. en_US
dc.contributor.author Sachs, Karen, Ph. D. Massachusetts Institute of Technology en_US
dc.contributor.other Massachusetts Institute of Technology. Biological Engineering Division. en_US
dc.date.accessioned 2007-08-30T15:26:21Z
dc.date.available 2007-08-30T15:26:21Z
dc.date.copyright 2006 en_US
dc.date.issued 2006 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/38865
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2006. en_US
dc.description Includes bibliographical references (p. 153-165). en_US
dc.description.abstract Cells communicate with other cells, and process cues from their environment, via signaling pathways, in which extracellular cues trigger a cascade of information flow, causing signaling molecules to become chemically, physically or locationally modified, gain new functional capabilities, and affect subsequent molecules in the cascade, culminating in a phenotypic cellular response. Mapping the influence connections among biomolecules in a signaling cascade aids in understanding of the underlying biological process and in development of therapeutics for diseases involving aberrant pathways, such as cancer and autoimmune disease. In this thesis, we present an approach for automatically reverse-engineering the structure of a signaling pathway, from high-throughput data. We apply Bayesian network structure inference to signaling protein measurements performed in thousands of single cells, using a machine called a flow cytorneter. Our de novo reconstruction of a T-cell signaling map was highly accurate, closely reproducing the known pathway structure, and accurately predicted novel pathway connections. The flow cytometry measurements include specific perturbations of signaling molecules, aiding in a causal interpretation of the Bayesian network graph structure. en_US
dc.description.abstract (cont.) However, this machine can measure only -4-12 molecules per cell, too few for effective coverage of a signaling pathway. To address this problem, we employ a number of biologically motivated assumptions to extend our technique to scale up from the number of molecules measured to larger models, using measurements of overlapping variable subsets. We demonstrate this approach by scaling up to a model of 11 variables, using 15 overlapping 4-variable measurements. en_US
dc.description.statementofresponsibility by Karen Sachs. en_US
dc.format.extent 165 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 http://dspace.mit.edu/handle/1721.1/7582
dc.subject Biological Engineering Division. en_US
dc.title Bayesian network models of biological signaling pathways en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Biological Engineering Division. en_US
dc.identifier.oclc 146356491 en_US


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