|Departments: Biochemistry and Information Technology
Phone: +353 87 952 0316
web: personal homepage
Bachelor of Veterinary Medicine & Surgery (B.V.M.S.) -Glasgow University.
Diploma in Mathematics -Open University.
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.
analysis of protein networks in yeast cells and their cellular
basis for this project
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 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 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:
computational structural model of the yeast protein network.
incorporation of existing data sets will be prioritised based upon evaluation of their respective
predictive power, network coverage, complementarity and other inherent bias . Such
data will include those from high-throughput experimental
techniques e.g. yeast two-hybrid ,
systematic affinity purification of tagged proteins, mass
spectrometric identification of associated proteins,
subcellular localisation ,
protein microarray data 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 & conserved neighbourhood predictions, and
from databases of protein complexes published singly in the
literature e.g. MIPS protein complexes.
This curated data archive will be stored
on a dedicated Linux server running Oracle – the ‘lowest’ tier
of a planned three-tier computational architecture.
network time series data integration & novel visualisation.
The ‘middle’ tier
will interface with the latest Systems Biology Workbench software following international
portability standards (e.g. SGML). 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 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 . Theoretical studies will be
performed to evaluate the potential of selected aspects of
established network formalisms such as graph theory & complexity
but also recent progress with groupoids  (flexible group theory) and
other novel network formalisms.
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 or
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 from the Institute of Molecular Biotechnology, Jena, Germany.
 Kitano, H. Computational systems biology. Nature 420,
 Han, J.D., Bertin, N., Hao, T., Goldberg,
D.S., Berriz, G.F., Zhang, L.V., Dupuy, D., Walhout, A.J.,
Roth, F.P. & Vidal, M. Evidence for dynamically organized
modularity in the yeast protein-protein interaction network. Nature 430,
 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 interactions. Nature 417,
 Gerstein, M., Lan, N. & Jansen, R. Proteomics. Integrating
interactomes. Science 295, 284-287 (2002).
 Uetz, P., et. al. A comprehensive analysis of protein-protein
interactions in Saccharomyces cerevisiae. Nature 403,
 Ito, T., Chiba,
T., Ozawa, R., Yoshida, M., Hattori, M. & Sakaki, Y. A
comprehensive two-hybrid analysis to explore the yeast protein
interactome. Proc Natl Acad Sci U S A 98,
 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 complexes. Nature 415,
 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
spectrometry. Nature 415, 180-183 (2002).
 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
 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,
 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).
 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
 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,
 Dandekar, T., Snel, B., Huynen, M. & Bork,
P. Conservation of gene order: a fingerprint of proteins
that physically interact. Trends Biochem Sci 23,
 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).
 Fayyad, U.M., Grinstein, G.G. & Wierse,
A. Information visualization in data mining and knowledge
discovery (San Francisco, Calif. ; London : Morgan Kaufmann,
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 hybridization. Mol
Biol Cell 9, 3273-3297 (1998).
 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 cycle. Mol Cell 2, 65-73 (1998).
 Gross, J., and Yellen, J. Graph theory
and its applications. (CRC Press.1999).
 Mainzer, K. Thinking in Complexity (Springer, Berlin,
 Stewart, I. Networking
opportunity. Nature 427, 601-4 (2004).
 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).
 Stephen, Wolfram A new kind of science (Champaign,
IL : Wolfram Media : Turnaround, 2002).
Conferences & Events:
2nd International Conference on Pathways, Networks, and Systems:
Theory and Experiments,
October 15-20, 2004,
Veterinary Register of Ireland