Computational prediction of genetic systems in prokaryotes

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DNA sequencing constantly reveals vast numbers of genes that cannot be annotated with a function by their similarity to known genes (function by homology). These unknown genes seem to outnumber those that can be assigned a function. Predicting functional associations, such as those between genes whose products work in the same biochemical pathway, can help suggest functions to the products of orphan genes, and might also help refine functional annotations predicted by homology. The most reliable clue to functional associations in prokaryotes (Bacteria and Archaea) might be the prediction of operons, adjacent genes in the same DNA strand transcribed into a single messenger RNA. Functional associations between separated genes can be inferred from equivalent genes predicted to be in an operon in a different genome (operon rearrangements).The proposed research program focuses on three main objectives: (1) developing and testing strategies for organizing proteins into functional families; (2) improving and streamlining the prediction of operons and the transference of functional associations across genomes; (3) To study the evolution of genetic systems by following their associations and reassociations across genomes and metagenomes.Predicted functional associations can be organized into networks. The structure and properties of these networks can help identify probable false positives, and produce clusters of genes thus organized into potential functional groups. Gene products will be organized into protein families. In turn, these families will be organized into groups of functionally interacting families. Such organization might suggest later experimental examination of functions, and the prioritization of experimental work. Associations of uncommon genes to known functions, will help discover different ways in which life forms have solved similar problems. Unknown groups will also help discover different ways in which life has solved similar problems, or even lead to discovering novel biochemistries.Our results will be important for end users interested in finding novel genes involved in particular functions. For example, the work will point to novel genes associated with activities of environmental and industrial interest, such as groups of genes associated with cellulose degradation, useful in the biofuel industry; or with cellulose biosynthesis, useful in manufacturing of biodegradable items. The results will also impact the design of artificial biological systems. For example, we expect to find novel versions of existing functional modules, which would facilitate the selection of versions with predictable behaviours for biosystem engineering.