Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent Univ., 2007.Thesis (Master's) -- Bilkent University, 2007.Includes bibliographical references leaves 81-83.As the scientific curiosity shifts toward system-level investigation of genomicscale
information, data produced about cellular processes at molecular level has
been accumulating with an accelerating rate. Graph-based pathway ontologies
and databases have been in wide use for such data. This representation has made
it possible to programmatically integrate cellular networks as well as investigating
them using the well-understood concepts of graph theory to predict their structural
and dynamic properties. In this regard, it is essential to effectively query
such integrated large networks to extract the sub-networks of interest with the
help of efficient algorithms and software tools.
Towards this goal, we have developed a querying framework along with a number
of graph-theoretic algorithms from simple neighborhood queries to shortest
paths to feedback loops, applicable to all sorts of graph-based pathway databases
from PPIs to metabolic pathways to signaling pathways. These algorithms can
also account for compound or nested structures present in the pathway data, and
have been implemented within the querying components of Patika (Pathway
Analysis Tools for Integration and Knowledge Acquisition) tools and have proven
to be useful for answering a number of biologically significant queries for a large
graph-based pathway database.Çetintaş, AhmetM.S