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COMPUTATIONAL ANALYSIS, VISUALIZATION AND TEXT MINING OF METABOLIC NETWORKS

xinjian, qi
http://rave.ohiolink.edu/etdc/view?acc_num=case1378479338

Abstract Details

2013, Doctor of Philosophy, Case Western Reserve University, EECS - Computer and Information Sciences.
With the recent advances in experimental technologies, such as gas chromatography/mass spectrometry, the number of metabolites that can be measured in biofluids of individuals has markedly increased. Given a set of such measurements, a very common task encountered by biologists is to identify the metabolic mechanisms that lead to changes in the concentrations of given metabolites and interpret the metabolic consequences of the observed changes in terms of physiological problems, nutritional deficiencies, diseases. This thesis presents the steady-state metabolic network dynamics analysis (SMDA) approach in detail. Experimental evaluation of the SMDA tool against a mammalian metabolic network database is also presented. The query output space of the SMDA tool can be reduced via (i) larger number of observations exponentially reduce the output size, and (ii) exploratory search and browsing of the query output space is provided to allow users to search what they are looking for. SMDA is then applied to gene lethality testing. Compared with other methods that are used for gene lethality testing, the advantages of the SMDA algorithm are: (1) SMDA requires less input, and (2) does not make optimality assumptions. The algorithm has been tested on the genome scale reconstructed network of Trypanosoma cruzi and its gene lethality testing results taken as ground truth. Also, in this thesis, we study general framework of visualization tools as well as distinct features of each tool in the PathCase systems, namely PathCase-SB, PathCase-MAW editor, PathCase MAW, PathCase-SMDA, PathCase-RCMN, PathCase-Recon, and PathCase-MQL. Finally, this thesis proposes a number of metabolite/reaction identification techniques for Genome-Scale Reconstructed Metabolic Networks (GSRMN) (by matching metabolites/reactions to corresponding metabolites/reactions of a source model or data source). We employ a variety of computer science techniques that include approximate string matching, similarity score functions and filtering techniques, all enhanced by the underlying metabolic biochemistry-based knowledge. The proposed metabolite/reaction identification techniques are evaluated by an empirical study on four pairs of GSRMNs. Our results indicate that significant accuracy gains are made using the proposed metabolite/reaction identification techniques.
Gültekin Özsoyoglu (Advisor)
Andy Podgurski (Committee Member)
M. Cenk Cavusoglu (Committee Member)
Nicola Lai (Committee Member)
Z. Meral Özsoyoglu (Committee Member)
180 p.
Bioinformatics; Computer Science
METABOLIC NETWORKS; COMPUTATIONAL ANALYSIS; VISUALIZATION; TEXT MINING; GSRMN; Genome-Scale; Reconstructed

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Citations

  • xinjian, Q. (2013). COMPUTATIONAL ANALYSIS, VISUALIZATION AND TEXT MINING OF METABOLIC NETWORKS [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1378479338

    APA Style (7th edition)

  • xinjian, qi. COMPUTATIONAL ANALYSIS, VISUALIZATION AND TEXT MINING OF METABOLIC NETWORKS. 2013. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1378479338.

    MLA Style (8th edition)

  • xinjian, qi. "COMPUTATIONAL ANALYSIS, VISUALIZATION AND TEXT MINING OF METABOLIC NETWORKS." Doctoral dissertation, Case Western Reserve University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1378479338

    Chicago Manual of Style (17th edition)

case1378479338
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© 2013, some rights reserved.Creative Commons License COMPUTATIONAL ANALYSIS, VISUALIZATION AND TEXT MINING OF METABOLIC NETWORKS by qi xinjian 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 Case Western Reserve University School of Graduate Studies and OhioLINK.