An algorithmic approach to constructing the tissue-specific architecture
of cellular networks


Emerging challenges in data-enabled sciences pose significant
problems at the intersection of modeling, algorithms, statistics,
and application validation. Over the past decade, we have worked
on a number of problems on network modeling and analysis in systems
biology, including alignment, conservation, modularity,
overrepresented pathways, and emergent properties. In this talk,
I will present some of our recent results on a novel integration
of these solutions to the problem of dissecting the tissue-specific
architecture of cellular networks.

Budding yeast, S. cerevisiae, has been used extensively as a
model organism for studying cellular processes in evolutionarily
distant species, including humans. However, different human tissues,
while inheriting a similar genetic code, exhibit unique anatomical
and physiological properties. Various functions, and their underlying
biochemical processes that differentiate tissues are not well understood;
neither is the extent to which a unicellular organism, such as yeast,
can be used to model and control these processes within each tissue.
We propose a novel statistical framework to systematically quantify
the suitability of yeast as a model organism for different human
tissues. We develop a computational method for dissecting the               
human interactome into tissue-specific cellular networks. Using this,
we simultaneously partition the functional space of human genes,
and their corresponding pathways, based on their conservation both
across species and among different tissues. These subspaces are
studied in detail, and are related to the overall similarity of
each tissue with yeast. We show that human-specific subsets of
tissue-selective genes are significantly associated with the onset
and development of tissue-specific pathologies. Consequently, they
provide ideal candidates as drug targets for therapeutic interventions,
as well as a guide-line for humanized yeast models for studying
human disorders.


(Joint work with Shahin Mohammadi and Shankar Subramaniam).