  Biological organisms are composed of numerous interconnected biochemical
processes. Diseases occur when normal functionality of these processes is
disrupted. Thus, understanding these biochemical processes and their
interrelationships is a primary task in biomedical research and a prerequisite
for diagnosing diseases, and drug development. Scientists studying these
processes have identified various pathways responsible for drug metabolism, and
signal transduction, etc.
  Newer techniques and speed improvements have resulted in deeper knowledge
about these pathways, resulting in refined models that tend to be large and
complex, making it difficult for a person to remember all aspects of it. Thus,
computer models are needed to analyze them. We want to build such a system that
allows modeling of biological systems and pathways in such a way that we can
answer questions about them.
  Many existing models focus on structural and/or factoid questions, using
surface-level knowledge that does not require understanding the underlying
model. We believe these are not the kind of questions that a biologist may ask
someone to test their understanding of the biological processes. We want our
system to answer the kind of questions a biologist may ask. Such questions
appear in early college level text books.
  Thus the main goal of our thesis is to develop a system that allows us to
encode knowledge about biological pathways and answer such questions about them
demonstrating understanding of the pathway. To that end, we develop a language
that will allow posing such questions and illustrate the utility of our
framework with various applications in the biological domain. We use some
existing tools with modifications to accomplish our goal.
  Finally, we apply our system to real world applications by extracting pathway
knowledge from text and answering questions related to drug development.

Artificial Intelligence;Computational Engineering, Finance, and Science;Computation and Language