Equivalence-set genes partitioning using an evolutionary-DP approach

Computation of transitive-closure equivalence sets has recently emerged as an important step for building static and dynamic models of gene network from DNA sequences. We present an evolutionary-DP approach in which dynamic programming (DP) is embedded into a genetic algorithm (GA) for fitness function evaluation of small equivalence sets (with m genes) within a large-scale genetic network of n genes, where n > m. This approach reduces a computation-intensive optimal problem of high dimension into a heuristic search problem on nCm candidates. The DP computation of transitive closure forms the basic fitness evaluation for selecting candidate chromosomes generated by GA operators. By introducing bounded mutation and conditioned crossover operators to constrain the feasible solution domain, small transitive-closure equivalence sets for large genetic networks can be found with much reduced computational effort. Empirical results have successfully demonstrated the feasibility of our GA-DP approach for offering highly efficient solutions to large scale equivalence gene-set partitioning problem. We also describe dedicated GA-DP hardware using field programmable gate arrays (FPGAs), in which significant speedup could be obtained over software implementation.