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Home >> Research >> Bioinformatics
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Ronan M.T. Fleming
Departments: Biochemistry and Information Technology

Phone: +353 87 952 0316
e-mail: web: personal homepage

Bachelor of Veterinary Medicine & Surgery (B.V.M.S.) -Glasgow University.
Diploma in Mathematics -Open University.

Research Interests:
Computational Systems Biology: in particular, investigating the dynamics of biological networks, with special reference to timescale patterns. Application of the resulting concepts to elucidate common features of chronic disease pathogenesis with a view towards a more deterministic approach to therapeutics.
Assimilation of fundamental theory from as wide a scientific background as possible. Identification of reliable factual information at the intersection between these theoretical constructs and development of novel biological perception from the comparison.

PhD Project:
Computational analysis of protein networks in yeast cells and their cellular functions

The basis for this project
The optimal characteristics of Saccharomyces cerevisiae  as a well-defined system for comprehensive studies under controlled conditions, together with high-throughput technologies employed on a genome-wide scale, have given rise to large scale structural and dynamical data from the different levels of cellular complexity (genome, transcriptome, proteome and metabolome). The key to understanding broad cellular functions is integrative analysis of comprehensive data sets. Computational systems biology[1] focuses on the global integrated view of biological processes by combining analysis of data sets and results obtained from complementary approaches. Since protein-protein interactions are essential for all cellular processes I propose to investigate a major question in systems biology with particular reference to the cell cycle: To what degree are protein network structures dynamically organised[2] in response to internal regulatory cues as the cell progresses through the classical stages of the cell cycle? Put succinctly, is it possible to mathematically describe the different phases of the cell cycle system in terms of global protein network structure? This forms the central hypothesis of this proposal. Two main aspects of the proposed work were identified as follows:

Validated computational structural model of the yeast protein network.
The incorporation of existing data sets will be prioritised based upon evaluation of their respective predictive power, network coverage, complementarity and other inherent bias[3] [4]. Such data will include those from high-throughput experimental techniques e.g. yeast two-hybrid[5] [6], systematic affinity purification of tagged proteins[7], mass spectrometric identification of associated proteins[8], synthetic lethality[9], subcellular localisation[10] [11], protein microarray data[12] and data from the Genome Stability Cluster at NUI, Galway. In silico data will be sourced from computational predictions of interactions e.g. gene fusion[13] & conserved neighbourhood predictions[14],  and from databases of protein complexes published singly in the literature e.g. MIPS protein complexes[15]. This curated data archive will be stored on a dedicated Linux server running Oracle – the ‘lowest’ tier of a planned three-tier computational architecture.

Yeast network time series data integration & novel visualisation.
The ‘middle’ tier will interface with the latest Systems Biology Workbench[16] software following international portability standards (e.g. SGML)[17]. I plan to represent the abstract biological conceptualisation of a subcellularly localised protein as an isomorphic object-oriented ‘entity’. Each entity will be able to receive input from non-specific global gene expression time series data and process consistent output for three dimensional visualisation[18] of transcriptional time series. This object-oriented framework will facilitate the construction of larger networks of such entities. Evolving ‘systems’ will be tested with existing cell cycle transcriptional datasets[19] [20]. Theoretical studies will be performed to evaluate the potential of selected aspects of established network formalisms such as graph theory[21] & complexity theory[22] , but also recent progress with groupoids[23] [24] (flexible group theory) and other novel network formalisms[25]. This resulting design flexibility will allow for integration of novel simulation engines as yeast network parameters become available from future high-throughput experiments. The ‘top’ tier will use a model-view-controller framework with graphics implemented in an open source software development environment such as OpenGL[26] or VTK[27]. This project has joint supervision by Dr. Aaron Golden from the Dept. of Information Technology and Dr. Heinz-Peter Nasheuer from the Dept. of Biochemistry with theoretical systems biology support from Dr. Thomas Wilhelm[28] from the Institute of Molecular Biotechnology, Jena, Germany.

[1] Kitano, H. Computational systems biologyNature 420, 206-210 (2002).

[2] Han, J.D., Bertin, N., Hao, T., Goldberg, D.S., Berriz, G.F., Zhang, L.V., Dupuy, D., Walhout, A.J., Cusick, M.E., Roth, F.P. & Vidal, M. Evidence for dynamically organized modularity in the yeast protein-protein interaction networkNature 430, 88-93 (2004).

[3] von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S. & Bork, P. Comparative assessment of large-scale data sets of protein-protein interactionsNature 417, 399-403 (2002).

[4]  Gerstein, M., Lan, N. & Jansen, R. Proteomics. Integrating interactomesScience 295, 284-287 (2002).

[5] Uetz, P., et. al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiaeNature 403, 623-627 (2000).

[6] Ito, T., Chiba, T., Ozawa, R., Yoshida, M., Hattori, M. & Sakaki, Y. A comprehensive two-hybrid analysis to explore the yeast protein interactomeProc Natl Acad Sci U S A 98, 4569-4574 (2001).

[7] Gavin, A.C., Bosche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., Schultz, J., Rick, J.M., Michon, A.M., Cruciat, C.M., Remor, M., Hofert, C., Schelder, M., Brajenovic, M., Ruffner, H., Merino, A., Klein, K., Hudak, M., Dickson, D. & Rudi Functional organization of the yeast proteome by systematic analysis of protein complexesNature 415, 141-147 (2002).

[8] Ho, Y., Gruhler, A., Heilbut, A., Bader, G.D., Moore, L., Adams, S.L., Millar, A., Taylor, P., Bennett, K., Boutilier, K., Yang, L., Wolting, C., Donaldson, I., Schandorff, S., Shewnarane, J., Vo, M., Taggart, J., Goudreault, M., Muskat, B. & Alfara Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometryNature 415, 180-183 (2002).

[9] Tong, A.H., Evangelista, M., Parsons, A.B., Xu, H., Bader, G.D., Page, N., Robinson, M., Raghibizadeh, S., Hogue, C.W., Bussey, H., Andrews, B., Tyers, M. & Boone, C. Systematic genetic analysis with ordered arrays of yeast deletion mutants.  Science 294, 2364-2368 (2001).

[10] Huh, W.K., Falvo, J.V., Gerke, L.C., Carroll, A.S., Howson, R.W., Weissman, J.S. & O'Shea, E.K. Global analysis of protein localization in budding yeast. Nature 425, 686-691 (2003).

[11] Ghaemmaghami, S., Huh, W.K., Bower, K., Howson, R.W., Belle, A., Dephoure, N., O'Shea, E.K. & Weissman, J.S. Global analysis of protein expression in yeast. Nature 425, 737-741 (2003).

[12] Zhu, H., Bilgin, M., Bangham, R., Hall, D., Casamayor, A., Bertone, P., Lan, N., Jansen, R., Bidlingmaier, S., Houfek, T., Mitchell, T., Miller, P., Dean, R.A., Gerstein, M. & Snyder, M. Global analysis of protein activities using proteome chips.  Science 293, 2101-2105 (2001).

[13] Marcotte, E.M., Pellegrini, M., Thompson, M.J., Yeates, T.O. & Eisenberg, D. A combined algorithm for genome-wide prediction of protein function.  Nature 402, 83-86 (1999).

[14] Dandekar, T., Snel, B., Huynen, M. & Bork, P. Conservation of gene order: a fingerprint of proteins that physically interact.  Trends Biochem Sci 23, 324-328 (1998).

[15] Mewes, H.W., Frishman, D., Guldener, U., Mannhaupt, G., Mayer, K., Mokrejs, M., Morgenstern, B., Munsterkotter, M., Rudd, S. & Weil, B. MIPS: a database for genomes and protein sequences.  Nucleic Acids Res 30, 31-34 (2002).

[16] The Systems Biology Workbench

[17] The systems biology markup language (SBML)

[18] Fayyad, U.M., Grinstein, G.G. & Wierse, A. Information visualization in data mining and knowledge discovery (San Francisco, Calif. ; London : Morgan Kaufmann, 2002).

[19] Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D. & Futcher, B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridizationMol Biol Cell 9, 3273-3297 (1998).

[20] Cho, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T.G., Gabrielian, A.E., Landsman, D., Lockhart, D.J. & Davis, R.W. A genome-wide transcriptional analysis of the mitotic cell cycleMol Cell 2, 65-73 (1998).

[21] Gross, J., and Yellen, J. Graph theory and its applications. (CRC Press.1999).

[22] Mainzer, K. Thinking in Complexity (Springer, Berlin, 1994).

[23] Stewart, I. Networking opportunityNature 427, 601-4 (2004).

[24] Stewart, I., Golubitsky, M. & Pivato, M. Symmetry groupoids and patterns of synchrony in coupled cell networks.  SIAM J. Appl. Dyn. Syst. 2, 609-646 (2003).

[25] Stephen, Wolfram A new kind of science (Champaign, IL :  Wolfram Media : Turnaround, 2002).

[28] Theoretical Systems Biology Group

Conferences & Events:
2nd International Conference on Pathways, Networks, and Systems: Theory and Experiments, October 15-20, 2004, Crete, Greece

Professional Organisations:
Veterinary Register of Ireland

Department of Information Technology,
National University of Ireland, Galway, University Road, Galway, Ireland.
Phone: +353 (0)91 524411 ext 3549, E-mail: aaron.golden(AT)