http://hdl.handle.net/1721.1/72923 Tue, 18 Jun 2013 21:31:25 GMT 2013-06-18T21:31:25Z http://hdl.handle.net/1721.1/79146 Web servers, databases, and algorithms for the analysis of protein interaction networks Park, Daniel K. (Daniel Kyu) Understanding the cell as a system has become one of the foremost challenges in the post-genomic era. As a result of advances in high-throughput (HTP) methodologies, we have seen a rapid growth in new types of data at the whole-genome scale. Over the last decade, HTP experimental techniques such as yeast two-hybrid assays and co-affinity purification couple with mass spectrometry have generated large amounts of data on protein-protein interactions (PPI) for many organisms. We focus on the sub-domain of systems biology related to understanding the interactions between proteins that ultimately drive all cellular processes. Representing PPIs as a protein interaction network has proved to be a powerful tool for understanding PPIs at the systems level. In this representation, each node represents a protein and each edge between two nodes represents a physical interaction between the corresponding two proteins. With this abstraction, we present algorithms for the prediction and analysis of such PPI networks as well as web servers and databases for their public availability: 1. In many organisms, the coverage of experimental determined PPI data remains relatively noisy and limited. Given two protein sequences, we describe an algorithm, called Struct2Net, to predict if two proteins physically interact, using insights from structural biology and logistic regression. Furthermore, we create a community-wide web-resource that predicts interactions between any protein sequence pair and provides proteome-wide pre-computed PPI predictions for Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. 2. Comparative analysis of PPI networks across organisms can provide valuable insights into evolutionary conservation. We describe an algorithm, called IsoRank, for global alignment of multiple PPI networks. The algorithm first constructs an eigenvalue problem that models the network and sequence similarity constraints. The solution of the problem describes a k partite graph that is further processed to find the alignments. Furthermore, we create a communitywide web database, called IsoBase, that provides network alignments and orthology mappings for the most commonly studied eukaryotic model organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae. Thesis (S.M.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2013.; This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.; Cataloged from student-submitted PDF version of thesis.; Includes bibliographical references (p. 41-44). Tue, 01 Jan 2013 00:00:00 GMT http://hdl.handle.net/1721.1/79146 2013-01-01T00:00:00Z http://hdl.handle.net/1721.1/72967 A systems approach to uncovering the adaptive response of cancer to targeted therapies Randall, Adrian Joseph Tyrosine kinase inhibitors have significant promise in the fight to develop agents that can target cancer in a tumor-specific manner. A number of drugs have been and are currently in development to inhibit specific kinases that can mediate uncontrolled proliferation; however, an unfortunate eventuality for most patients receiving these treatments is the development of resistance that renders these drugs almost completely ineffective. While a number of mechanisms can evolve within a tumor to mitigate effects of kinase inhibitors, we sought to uncover what changes are occurring in the tyrosine phosphorylation network at both short timescales (minutes to 72 hours) and long timescales (120 hours+) that can be playing a role in helping a tumor become resistant to driver-kinase inhibition. It is our hypothesis that specific feedback networks are able to detect and overcome driver kinase inhibition through activation of potential other pathways, which can go on to mediate a longer term resistance phenotype. In order to probe dynamics in the tyrosine phosphorylation network, we employed mass spectrometry to analyze peptides derived from six non-small cell lung cancer cell lines that we classify as either EGFR+ or EML4-ALK+. From both mass spectrometry data and growth assays, we identified an unintuitive boost in signaling and growth in response to low inhibitor concentrations, suggestive of a cellular mechanism that is adaptive to driver kinase inhibition. Studies of EML4-ALK driven H3122 cells showed that this short-term response is not the same as the known long-term resistance mechanism to ALK inhibition, leading support to the notion that the short-term "adaptive response" may be a novel type of mechanism to aid tumor adaptation to targeted therapies. In an effort to better probe signaling events occurring downstream of the phosphotyrosine network, a new pull down technique for mass spectrometry using 14-3-3 protein against phosphoserine and phosphothreonine peptides is described. The results of these studies open up many potential avenues for further exploration into the immediate and long-term signaling response of cancer to targeted therapies. Thesis (S.M.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2012.; Cataloged from PDF version of thesis.; Includes bibliographical references (p. 47-53). Sun, 01 Jan 2012 00:00:00 GMT http://hdl.handle.net/1721.1/72967 2012-01-01T00:00:00Z