Improved PCR design for mouse DNA by training finite state machines

Abstract

This project presents an updated method for classification of polymerase chain reaction primers in mice using finite state classifiers. This is done to compensate for many lab, organism and chemical specific factors that are costly. Using Finite State Classifiers can help decrease the number of primers that fail to amplify correctly. For training these classifiers, five different evolutionary algorithms that use an incremental fitness reward are used. Variations to the number of generations and the values in the fitness reward are examined, and the resulting designs are presented. By controlling the fitness reward correctly, there is a potential to develop classifiers with a high likelihood of accepting only good primers. The proposed tool can act as a post-production add-on to the standard primer picking algorithm for gene expression detection in mice to compensate for local factors that may induce errors.