Minimum Set Primers and Unique Probes Design Algorithms for

Differential Detection of Symptom-Related Pathogens

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Introduction
Methodology
	Tetra-Nucleotide Nucleation (TNN)
	Unique & Common Sections
	Nearest-Neighbor Model
	MCGA
	Linker Design
Computational Results
Bio-Experiment
Conclusion
Reference

The reduction of multiple-use primers will significantly reduce the cost of microarray or diagnostic PCR analysis. Here we formulate the reduction of multiple-use primers as a constrained set-covering problem. That is, we try to optimize the number of primers so that we can use minimum primers for the target sequences. We have used the Modified Compact Genetic Algorithm (MCGA) to solve the set covering problem. Genetic algorithms have been used for designing primers (Wu, et al., 2004) , but in this work, the purpose of MCGA is to reduce the number of redundant primers. Formulation of Multiple-Use Primer Design as a Set Covering Problem Modified Compact Genetic Algorithm