Switching Between Discrete and Continuous Models To Predict Genetic Activity

Molecular biologists use a variety of models when they predict the behavior of genetic systems. A discrete model of the behavior of individual macromolecular elements forms the foundation for their theory of each system. Yet a continuous model of the aggregate properties of the system is necessary for many predictive-tasks. I propose to build a computer program, called PEPTIDE, which can predict the behavior of moderately complex genetics systems by performing qualitative simulation on the discrete model, generating a continuous model from the discrete model through aggregation, and applying limit analysis to the continuous model. PEPTIDE's initial knowledge of a specific system will be represented with a discrete model which distinguishes between macromolecule structure and function and which uses five atomic processes as its functional primitives. Qualitative Process (QP) theory [Forbus 83] provides the representation for the continuous model. Whenever a system has multiple models of a domain, the decision of which model to use in a given time becomes a critically important issue. Knowledge of the relative significance of differing element concentrations and the behavior of process structure cycles will allow PEPTIDE to determine when to switch reasoning modes. A.I. Laboratory Working Papers are produced for internal circulation, and may contain information that is, for example, too preliminary or too detailed for formal publication. It is not intended that they should be considered papers to which reference can be made in the literature.