Genetic algorithm approach for the closest string problem

A fundamental aspect of post-transcriptional gene silencing (PTGS) or RNA interference (RNAi) is the requirement of sequence homology between the transgene and viral or messenger RNAs being targeted. For example, virus-resistant transgenic plants are resistant only to viruses that are closely related (i.e. high sequence homology) to the virus from which the transgene was derived. One idea for broadening this resistance is to devise an artificial sequence that incorporates the sequence variation found in a viral population. This requires an algorithm which can determine an artificial sequence with an optimal (or at least a 90-95% ) homology to all of the viral sequences in a population. The genetic algorithm (GA) presented in this paper serves this purpose. It should be of great value to all researchers who utilize PTGS or RNAi. In the context of coding theory, the task is to find the radius of a code S ⊂ {A, C, G, T} ⁿ. In computational biology this problem is commonly referred to as the closest string problem. Experimental results suggest that this NP-complete optimization problem can be approached well with a custom-built GA.