Determining conserved motifs that distinguish high-expressed introns from low-expressed introns with a genetic algorithm on the C. elegans genome

Student Name: 
Allen Mao
UCD Department: 
Molecular & Cellular Biology
UCD Mentor: 
Ian Korf

In a wide variety of eukaryotes, some introns elevate mRNA accumulation to increase gene expression. However, the biological mechanism for this process is not well understood and it is unknown why some introns have this effect while others lack it. In this research, the object is to have a better understanding of intron function to gain greater control over gene expression by creating a genetic algorithm that identifies sequence motifs that are more commonly found in introns of high expression than introns of low expression. The approach for creating the genetic algorithm consists of generating random motifs, selecting fit motifs as per an objective and fitness function, and randomly mating and mutating motifs in order to yield high surviving motifs against the objective and fitness function.