Intelligent decision support for protein crystal growth

Current structural genomics projects are likely to Produce hundreds of prot eins a year for structural analysis. The primary goal of our research is to speed up the process of crystal growth for proteins in order to enable the determination of protein structure using single crystal X-ray diffraction. We describe Max, a working prototype that includes a high-throughput cryst allization and evaluation setup in the wet laboratory and an intelligent so ftware system in the computer laboratory. A robotic setup for crystal growt h is able to prepare and evaluate over 40 thousand crystallization experime nts a day. Images of the crystallization outcomes captured with a digital c amera are processed by an image-analysis component that uses the two-dimens ional Fourier transform to perform automated classification of the experime nt outcome. An information repository component, which stores the data obta ined from crystallization experiments, was designed with an emphasis on cor rectness, completeness, and reproducibility. A case-based reasoning compone nt provides support for the design of crystal growth experiments by retriev ing previous similar cases, and then adapting these in order to create a so lution for the problem at hand. While work on Max is still in progress, we report here on the implementation status of its components, discuss how our work relates to other research, and describe our plans for the future.