Molecular systems biology is an emerging discipline aimed at
understanding cellular function at the systems level. A prerequisite
to this systems level understanding will be to understand the
relationships between proteins, DNA and RNA. These relationships can
be identified in terms of physical interactions that underlie various
cellular processes (e.g., metabolism, signaling, regulation of
molecular activity), as well as indirect functional association, such
as genetic interactions or synthetic lethality. These relationships
are often abstracted using network models, which provide high level
descriptions of the organization of the cell. Graph theoretical
analyses of molecular networks show that, many substructures with
characteristic topologies (e.g., feed-back or feed-forward loops),
called network motifs, occur significantly more often in these
networks than would be expected by chance. However, little is known
about the functionality of these motifs in terms of the cellular
dynamics that underlie the networks. The focus of this project will be
to understand the functional and evolutionary significance of motifs
in biological networks, through comparative and integrative analysis
of networks that capture different aspects of cellular organization.
In particular, our goals are to:
Characterize different types of molecular networks (e.g.,
protein-protein interactions, genetic interactions, regulatory
interactions, metabolic networks) in terms of their motif
composition.
Develop novel metrics for evaluating the statistical significance
of network motifs, with a view to assessing their modularity in terms
of the coupling of their building blocks (as opposed to sole
occurrence count).
Understand possible functional and evolutionary significance of
the observed motifs.
The project will be co-supervised by Drs Mehmet Koyuturk (Electrical
Engineering & Computer Science) and Rob Ewing (Center for Proteomics
and Bioinformatics) and will enable students to participate in the
exciting cross-campus Systems Biology research community. In addition,
students can expect to become familiar with graph theory, principles
of cellular signaling and regulation, and biological network
databases.
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