Representing, Analyzing, and Synthesizing Biochemical Pathways

Abstract

Living cells are complex systems whose growth and existence depends on thousands of biochemical reactions. A subset of these reactions | the metabolism | interconverts small molecules. A variety of computational problems arise in representing knowledge of the metabolism in electronic form, in analyzing that knowledge to gain deeper insights into complexities of the metabolism, and in using such knowledge in biology, biotechnology and health applications. These problems provide a rich set of opportunities for exploiting existing AI techniques, and challenges for developing new and improved techniques. This article describes challenges and opportunities for addressing computational problems in the metabolism with techniques from knowledge representation, planning, integration of heterogeneous databases, qualitative reasoning, knowledge acquisition, and machine learning. The computational problems include construction of large shared knowledge bases of biochemical pathways, knowledge acquisition from the biochemical literature, qualitative simulation of metabolic pathways, thermodynamic estimation, synthesis of metabolic pathways, and scientific hypothesis formation.