Constraint matching as a means of designing biochemical experiments in multi-enzyme systems.
A method of qualitative analysis by constraint matching, where expectations derived from theory or from data bases are systematically compared against experimental findings, is described. This was originally developed as an artificial intelligence technique to analyze enzyme kinetic mechanism determinations. It is shown to have been used (without computer involvement) in designing experiments involving a few enzymes, and is suggested as a useful experimental design tool. The experiments in question validate the behavior of insulinoma extracts as models for pancreatic islet glycolysis, which conform to the expectations from the relevant enzyme literature. Possible generalization to other areas of biological research is suggested.
Garfinkel
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Department of Computer Science, University of Pennsylvania, Philadelphia 19104.
D004798:Enzymes / Q000378:metabolism*; D005941:Glucokinase / Q000378:metabolism; D005956:Glucose-6-Phosphate Isomerase / Q000378:metabolism; D006019:Glycolysis; D006593:Hexokinase / Q000378:metabolism; D007340:Insulinoma / Q000201:enzymology; D007515:Islets of Langerhans / Q000378:metabolism*; D007700:Kinetics; D008954:Models, Biological; D010732:Phosphofructokinase-1 / Q000378:metabolism; D014407:Tumor Cells, Cultured
D004798:Enzymes; D006593:Hexokinase; D010732:Phosphofructokinase-1; D005941:Glucokinase; D005956:Glucose-6-Phosphate Isomerase