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Identifying Protein Functions and Biological Systems through Exploring Biological Networks

Shih, Yu-Keng
http://rave.ohiolink.edu/etdc/view?acc_num=osu1388676152

Abstract Details

2014, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
Advances in biological high-throughput technology have led to an increased amount of available data on various biological interactions, including protein-protein interaction (PPI), protein-DNA/RNA interaction, genetic interaction, drug-target interaction. Recently, these high-throughput approaches have enabled researchers to discover the underlying sub-systems, including regulatory pathways, protein complexes, and functional modules, in order to identify the role of each protein or gene. Moreover, examining these interaction data and functional annotations in different organisms can assist in understanding how the proteins and genes have evolved across species. Generally, the central objective is to deduce how sub-systems and whole organisms work through exploring the biological networks, in which a node is a protein or gene and an edge mimics an interaction or other relationship. In this thesis, we seek to discover functional modules, regulatory pathways, and functional orthologs by computational approaches. First, as the graph clusters in a biological network can be considered as functional modules, we develop several functional modules identification algorithms based on Markov graph clustering algorithm (MCL) to process different datasets. The challenges here are: (1) Functional modules may exhibit overlapping characteristics. (2) Some biological experiments might produce negative evidence showing that two proteins are not likely to be in the same cluster, resulting in edges with negative weight in a network. (3) Biological data might provide the direction of the interaction between genes and proteins, resulting in directed edges in the biological network. As existing graph clustering algorithms cannot incorporate these biological characteristics, we propose three new variations of MCL to correctly generate clusters. Second, genes and proteins might form a linear regulatory pathway, in which nodes activate or deactivate each other. We propose the use of k shortest path algorithm to evaluate the importance of the relationship between nodes. We develop a single-source k shortest path algorithm while most k shortest algorithms focused on the single-pair problem. We moreover introduce diversity into k paths, resulting in more functionally consistent pathways. Third, we develop a global alignment algorithm for discovering functional orthologs conserved in different species. The algorithm we develop is based on clustering methods and graph matching techniques in order to detect conserved interactions while simultaneously attempting to maximize the sequence similarity of nodes involved in the alignment. In each of the above proposed algorithm, we perform experiments on several network datasets. Comparing with existing algorithms on the known gold standard biological function annotations, we show that our methods can enhance the accuracy and/or the efficiency of predicting protein functions.
Srinivasan Parthasarathy (Advisor)
Yusu Wang (Committee Member)
Han-Wei Shen (Committee Member)
203 p.
Computer Science

Recommended Citations

Citations

  • Shih, Y.-K. (2014). Identifying Protein Functions and Biological Systems through Exploring Biological Networks [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1388676152

    APA Style (7th edition)

  • Shih, Yu-Keng. Identifying Protein Functions and Biological Systems through Exploring Biological Networks . 2014. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1388676152.

    MLA Style (8th edition)

  • Shih, Yu-Keng. "Identifying Protein Functions and Biological Systems through Exploring Biological Networks ." Doctoral dissertation, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1388676152

    Chicago Manual of Style (17th edition)

osu1388676152
953
© 2014, some rights reserved.Creative Commons License Identifying Protein Functions and Biological Systems through Exploring Biological Networks by Yu-Keng Shih is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.