drosophila

14 anni fadiscuss

Right now experimentalists generate data and then try to find computer people to analyse their data. However, in the not too distant future computer models will drive experimental research as hypothesis generation tools. Then the computer people will be seeking biology people ( or robots) to run experiments to validate their hypothesis and there will be more respect for the field.

drosophila

14 anni fadiscuss

1. gene networks is a big one: some proteins turn genes on or off. Some of those genes get translated into other proteins that turn genes on or off. How can you infer the interactions from experimental data? How can you figure out what these complex networks DO? 2. Predicting gene expression: where do proteins bind to the DNA? How can you predict what these proteins do once they are bound ( add chemical tags to structural proteins, knock off structural proteins by bending DNA, etc)? How can you predict how frequently the gene will be transcribed? How does the 3D shape of the DNA effect this?

These are just two of many questions ( biased towards my research interests of course ). It is really funny that he mentions sequence alignment and phylogenetically as the two big problems, because people generally consider these to be boring, uncool, solved-well-enough-for-our-purposes problems nowadays and just trust the algorithms described by Durbin decades ago. It sounds like the writer really doesn't know bioinformatics that well...

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