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In Hunter, L. (Ed.), Artificial Intelligence in Molecular Biology, AAAI Press, Menlo Park, CA.1993, pp. 225-233.

Knowledge-Based Simulation of DNA metabolism: Prediction of Action and Envisionment of Pathways

A. R. Galper, D. L. Brutlag and D. H. Millis

Department of Biochemistry & the Section on Medical Informatics, Stanford University School of Medicine, Stanford, California 94305-5307.

Our understanding of any process can be measured by the extent to which a simulation we create mimics the real behavior of that process. Deviations of a simulation indicate either limitations or errors in our knowledge. In addition, these observed differences often suggest verifiable experimental hypotheses to extend our knowledge.

The biochemical approach to understanding biological processes is essentially one of simulation. A biochemist typically prepares a cell-free extract that can mediate a well-described physiological process. The extract is then fractionated to purify the components that catalyze individual reactions. Finally, the physiological process is reconstituted in vitro. The success of the biochemical approach is usually measured by how closely the reconstituted process matches physiological observations.

An automated simulation of metabolism can play a role analogous to that of the biochemist in using and extending knowledge. By carefully representing the principles and logic used for reasoning in the laboratory, we can simulate faithfully, on a computer, known biochemical behavior. The simulation can also serve as an interactive modeling tool for reasoning about metabolism in the design of experiments, in discovery, and in education.

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