UMBC High Performance Computing Facility
Please note that this page is under construction. We are documenting the
240-node cluster maya that will be available in Spring 2014. Currently,
the cluster tara is still available. Please see the 2013 Resources Pages
under the Resources tab.
Analysis of Promoter Regions with Soft-Computing Techniques
Ivan Erill, Department of Biological Sciences
The understanding of promoter sequences is key to the further advance of genomics, since promoters constitute the fundamental blocks of transcriptional regulation, thus providing information about the transcriptional interactions among the genes they regulate. The analysis of promoter sequences is a complicated because promoters, as
opposed to coding regions, show a great variability in their organization, structure and usage of the transcription factor alphabet of operators available in a cell. Traditional apporaches to promoter modeling are usually top-down and typically impose rigid rules on promorer structure that are not fitted for the general case, thereby restricting their application. In the light of this, soft-computing methods, such as artificial neural networks and genetic algorithms, seem clearly indicated to approach the modeling of promoters in a bottom-up, data-driven model.