Investigating an Artificial Immune System to Strengthen Promoter Region Structure Prediction and Promoted Region Identification Using Cellular Automata Classifier

Genes carry the instructions for making Promoter Regions that are found in a cell as a specific sequence of nucleotides that are found in DNA molecules. But, the regions of these genes that code for Promoter Regions may occupy only a small region of the sequence. Identifying the promoter regions play a vital role in understanding these genes. In this paper we have explored an artificial immune system can be used to strengthen and identify the Promoter Region coding regions in genomic DNA system in changing environments, and Cellular Automata (CA) technique for Promoter Region structure prediction of small alpha/beta Promoter Regions using Rosetta. From an initial round of Rosetta sampling, we learn properties of the energy landscape that guide a subsequent round of sampling toward lower-energy structures. Three different approaches to improve tertiary fold prediction using the genetic algorithm are discussed: (i) Refinement of the search strategy, (ii) combination of prediction and experiment and (iii) inclusion of experimental data as selection criteria into the genetic algorithm. It has been developed using a slight variant of genetic algorithm. Good classifier can be produced especially when the number of the antigens is increased. However, an increase in the range of the antigens had