Genome optimization and evolution simulation using genetic algorithms and GA-TRMR

There are various desirable traits in organisms that humans wish to improve. To change a trait, the genetic background affecting that trait must be manipulated. In this thesis, genetic algorithms (GA) is suggested as a search strategy to efficiently sample from the combinatorial sequence space to identify the optimum genome sequence. Also, we proposed a restricted form of GA that approximates the search of a Trackable Multiplex Recombineering (TRMR) based approach, and is named GA-TRMR. The performance of GA and GA-TRMR strategies are simulated on various synthetic fitness landscapes through generations. The landscapes are constructed using fitness functions modeling for various degrees of epistasis. Both algorithms demonstrated viability for finding the optimum sequence. GA-TRMR specifically, when is equipped with a form of local sampling algorithm along with a selite set, turns into a powerful search algorithm which could find the optimum genome sequence in interaction with TRMR