Artificial intelligence methods for theory representation and hypothesis formation

Abstract

This article describes artificial intelligence methods for representing theories in molecular biology, and for improving the predictive power of these theories using experimental data. A program called GENSIM provides a framework for representing theories that includes descriptions of classes of biological objects (genes, enzymes, etc.), and processes that specify potential interactions among these objects (such as enzymatic reactions). GENSIM can employ a theory specified within this framework to predict the outcomes of biological experiments. A program called HYPGENE comes into play when the observed outcome of an experiment does not match the outcome predicted by GENSIM. HYPGENE works backward from the error in GENSIMs prediction to postulate changes to both the theory embodied by GENSIM, and the presumed initial conditions of the experiment. I view HYPGENEs hypothesis generation task as a design problem, and I have adapted AI methods developed for design and planning to this task. These techniques were developed in conjunction with an in-depth study of the discovery of the gene regulation mechanism of attenuation in the E. coli tryptophan operon. Both GENSIM and HYPGENE have been tested on sample problems from the history of attenuation, and produced many of the same solutions as biologists did.