| Date |
March 10, 2008 |
| Speaker |
Dr. Koji Tsuda, Max Planck Institute for Biological Cybernetics, Germany |
| Title |
Enumeration of Condition-Specific Dense Modules in Protein Interaction Networks |
| Abstract |
Modern systems biology aims at understanding how the
different molecular components of a biological cell interact. Often,
cellular functions are performed by complexes consisting of many
different proteins. The composition of these complexes may change
according to the cellular environment, and one protein may be
involved in several different processes. The automatic discovery of
functional modules in protein interaction data is challenging. While
previous approaches use approximations to extract dense modules,
our approach exactly solves the problem of dense module enumeration.
Furthermore, constraints from additional information sources
such as gene expression and phenotype data can be integrated,
so we can systematically mine for dense modules with interesting
profiles. Given a weighted protein interaction network, our method
discovers all modules that satisfy a user-defined minimum density
threshold. We employ a reverse search strategy, which allows
us to exploit the density criterion in an efficient way. Our experiments
show that the novel approach is feasible and produces
biologically meaningful results. In validation studies using yeast
data, the new method achieved a higher coverage than clique-based
approaches, while maintaining a high reliability level. Moreover, we
enhanced the yeast network by phenotypic and phylogenetic profiles
and the human network by tissue-specific expression data to
investigate profile-consistent modules. The resulting sets of modules
revealed condition-specific reorganization of complexes as well
as inter-complex associations.
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