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Applications

In addition to having potential utility for incorporating localization information into molecular biology databases, the methods described here may be of value in a number of automated or "high throughput" screening approaches. First, automated methods are needed to screen the vast number of compounds now available as potential pharmaceuticals. The classification of protein localization patterns will be of use here as a means of identifying those cells that have responded in a desired way to the application of a particular compound. It will be possible, for instance, to automatically identify only those cells in which the applied compound traps a surface receptor in the ER, or in which that compound prevents translocation of a transcription factor to the nucleus. Second, it should be possible to automatically screen for cells displaying a mutant phenotype. In this case, one might ask the computer to identify those cells that have a malformed Golgi apparatus. Third, one might screen a population of live cells for those members that are in a particular stage of the cell cycle, image those cells repeatedly until the event under study is complete, and then begin screening again.

A last potentially interesting application of automated localization analysis is in the area of gene discovery. By using molecular techniques to randomly insert visualizable tags into a wide variety of genes, it is possible to generate localization patterns for a large number of proteins, some known and some unknown. Once a large number of cells have had a single protein tagged, images of protein localization can be collected. This approach, with manual screening of the resulting patterns, has been used to determine localization patterns for randomly-tagged genes from yeast using gene fusions with either LacZ [50] or Green Fluorescent Protein [51]. Automation of the pattern analysis would potentially speed this approach, and one can then conceive of a localization database for all expressed proteins in yeast. This is beyond current capabilities for organisms with larger genomes, but screens for proteins with particular patterns can be imagined (e.g. ER proteins). While this may be carried out manually, the number of patterns requiring screening in this scheme is large and automated identification of patterns of interest is desirable. The results presented here suggest that such an approach is feasible.


next up previous contents
Next: Bibliography Up: Conclusions Previous: Future Work
Copyright ©1999 Michael V. Boland
1999-09-18