Systems Toxicology: Mining chemical-toxicity signaling paths to enable network medicine

Systems toxicology, a branch of toxicology that studies chemical effects on biological systems, presents exciting knowledge discovery challenges for the information researcher. The exponential increase in availability of genomic and proteomic data in this domain needs to be matched with increasingly sophisticated network analysis approaches. Improved ability to mine complex gene and protein interaction networks may eventually lead to discovery of drugs that target biological sub-networks (‘network medicine’) instead of individual proteins. In this thesis, we have proposed and investigated the use of a maximal edge centrality criterion to discover drug-toxicity signaling paths inside a human protein interaction network. The signaling path detection approach utilizes drug and toxicity information along with two novel edge weighting measures, one based on edge centrality for detected paths and another using differential gene expression between tissues treated with toxicity-inducing drugs and a control set. Drugs known to induce non-immune Neutropenia were analyzed as a test case and common path proteins on discovered signaling paths were evaluated for toxicological significance. In addition to investigating the value of topological connectivity for identification of toxicity biomarkers, the gene expression-based measure led to identification of a proposed biomarker panel for screening new drug candidates. Comparative evaluation of findings from the DTSP approach with standard microarray analysis method showed clear improvements in various performance measures including true positive rate, positive predictive value, negative predictive value and overall accuracy. Comparison of non-immune Neutropenia signaling paths with those discovered for a control set showed increased transcript-level activation of discovered signaling paths for toxicity-inducing drugs. We have demonstrated the scientific value from a systems-based approach for identifying toxicity-related proteins inside complex biological networks. The algorithm should be useful for biomarker identification for any toxicity assuming availability of relevant drug and drug-induced toxicity information.Ph.D., Information Studies -- Drexel University, 201

The structure and function of biological networks

Biology has been revolutionized in recent years by an explosion in the availability of data. Transforming this new wealth of data into meaningful biological insights and clinical breakthroughs requires a complete overhaul both in the questions being asked and the methodologies used to answer them. A major challenge in organizing and understanding the data is the ability to define the structure in biological systems, especially high level structures. Networks are a powerful and versatile tool useful in bridging the data and the complex biological systems. To address the importance of the higher-level modular and hierarchical structure in biological networks, we have investigated in this thesis the topological structure of protein-protein interaction networks through a comprehensive network analysis using statistical and computational techniques and publicly available protein-protein interaction data sets. Furthermore, we have designed and implemented a novel and efficient computational approach to identify modules from a seed protein. The experiment results demonstrate the efficiency and effectiveness of this approach in finding a module whose members exhibit high functional coherency. In addition, toward quantitative studies of protein translation regulatory networks (PTRN), we have developed a novel approach to reconstruct the PTRN through integration of protein-protein interaction data and Gene Ontology annotations. We have applied computational techniques based on hierarchical random graph model on these reconstructed PTRN to explore their modular and hierarchical and to detect missing and false positive links from these networks. The identification of the high order structures in these networks unveils insights into their functional organization.Ph.D., Information Science and Technology -- Drexel University, 201

Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics

Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.Comment: 25 pages with 6 figures and a Glossary + Supporting Information containing pseudo-codes of all algorithms used, 14 Figures, 5 Tables (with 18 module definitions, 129 different modularization methods, 13 module comparision methods) and 396 references. All algorithms can be downloaded from this web-site: http://www.linkgroup.hu/modules.ph

IEEE TRANSACTIONS ON FUZZY SYSTEMS 1 A Novel Approach for Mining and Fuzzy Simulation of Subnetworks From Large Biomolecular Networks

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