Understanding Bio-molecular Networks of Cellular Processes Using Computational Biology Approach

Mandloi, Sapan (2017) Understanding Bio-molecular Networks of Cellular Processes Using Computational Biology Approach. PhD thesis, C U.


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    Chakrabarti, Saikat


    While experimental research forms the foundation of biological science, computational biology concepts and mathematical models have become crucial to understand the observed phenomena underlying complex systems. Particularly in molecular biology, mathematical models of networks help to extend knowledge of single interactions and connect to a systems-level analysis. Well-structured knowledge database source for interaction and other biomedical data are important for systems level analysis. This knowledge database source plays important role in simplifying the access to information and its further processing. In biomedical domain, results and other important information (like association between bio-entity terms) are present in the form of unstructured text. To advance the scientific research it is crucial to extract the information from unstructured to structured format. I have developed a literature and data mining platform PALM-IST (Pathway Assembly from Literature Mining - an Information Search Tool). This platform provides users to extract biomedical research articles for their searched term and at the same time build relationship/pathways between bio-entity terms like genes, proteins, drugs, diseases and biological processes. Information is extracted using literature mining methods i.e. from unstructured text (literature resources like PubMed) and from existing databases i.e. structured resources (like protein-protein interaction database STRING and pathway resources KEGG and other). The main theme of this thesis is modeling and analysis of biological networks. In this thesis different types of networks are analyzed using context specific experimental datasets (like expression data) and mathematical models. Further a systems biology based network information flow model unlike previous is developed and discussed. The resulting model was utilized to study the roles of constituently active EGFR (EGFRvIII mutated) in GBM (Glioblastoma) condition. Model developed for information flow analysis incorporates experimental data, biological information and network properties into biological network analysis for identification of molecular signatures of disease phenotypes. These signature proteins and connections identified from the model were further tested and validated using cell culture experiments. The model is flexible to apply on diverse data and conditions in a combination with a suitable molecular network. By the integration of diverse sources of information and molecular events from different points of view, new and exhaustive insights into biological processes can be acquired

    Item Type: Thesis (PhD)
    URI: http://www.eprints.iicb.res.in/id/eprint/2662
    Subjects: Structural Biology & Bioinformatics
    Divisions: Indian Institute of Chemical Biology
    Depositing User: Ms Sutapa Ganguly
    Date Deposited: 26 May 2017 15:46
    Last Modified: 26 May 2017 15:46

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