Qualitative Knowledge Models in Functional Genomics and Proteomics

Predicting pathological phenotypes based on genetic mutations remains a fundamental and unsolved issue. When a gene is mutated, the molecular function- ality of the gene product may be affected and many cellular processes may go awry. Basic molecular functions occur in networks of interactions and events that produce subsequent cellular and physiological functions. Most knowledge of these interactions is represented diffusely in the published literature, Excel lists, and specialized relational databases and so it is difficult to assess our state of under- standing at any moment. Thus it would be very useful to systematically store knowl- edge in data structures that allow the knowledge to be evaluated and examined in detail by scientists as well as computer algorithms. Our goal is to develop technol- ogy for representing qualitative, noisy, and sparse biological results in support of the eventual goal of fully accurate quantitative models. In a recent paper, we described an ontology that we developed for modeling bio- logical processes (1). Ontologies provide consistent definitions and interpretations of concepts in a domain of interest (e.g., biology) and enable software applications to share and reuse the knowledge consistently (2). Ontologies can be used to perform logical inference over the set of concepts to provide for generalization and expla- nation facilities (3). Our biological process ontology combines and extends two existing components: a workflow model and a biomedical ontology, both described in the methods and tools section. Our resulting framework possesses the following properties: (1) it allows qualitative modeling of structural and functional aspects of a biological system, (2) it includes biological and medical concept models to allow for querying biomedical information using biomedical abstractions, (3) it allows