Representing, reasoning and answering questions about biological pathways - various applications

Biological organisms are made up of cells containing numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted, manifesting as disease symptoms. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for activities including diagnosing diseases, and drug development. Scientists studying these interconnected processes have identified various pathways involved in drug metabolism, diseases, and signal transduction, etc. Over the last decade high-throughput technologies, new algorithms and speed improvements have resulted in deeper knowledge about biological systems and pathways, resulting in more refined models. These refined models tend to be large and complex, making it difficult for a person to remember all aspects of it. Thus, computer models are needed to represent and analyze them. The refinement activity itself requires reasoning with a pathway model by posing queries against it and comparing the results against a real biological system. We want to model 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, relying on surface-level information 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 be able to answer the kind of questions a biologist may ask. So, we turned to early college level text books on biology for such questions. 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 that demonstrate 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 use our question answering system in real world applications by extracting pathway knowledge from text and answering questions related to drug development.