Advantages of a Pareto-Based Genetic Algorithm to Solve the Gene Synthetic Design Problem
Jose Luis Oliveira.
Codon usage, codon context, rare codons, nucleotide repetition and mRNA destabilizing sequences are but a
few of the many factors that influence the efficiency of protein synthesis. Therefore, gene redesign for heterologous
expression is a multi-objective optimization problem and the factors that need to be considered are often conflicting.
Evolutionary approaches have already been shown to be able to evolve a sequence under the forces of specific constraints.
However, it is unclear what are the advantages of a slower algorithm such as GA when compared with other faster
algorithms in the gene redesign context.
Here, a solution using genetic algorithms along with a Pareto archive is used for the gene synthetic redesign problem. The
different redesign parameters are merged using an adapted genetic algorithm strategy. From the created model, the best
possible synonymous gene sequence is generated. This allows tackling the gene redesign problem by exploring the large
search space of possible synonymous sequences. It is then shown that genetic algorithms have several advantages over
other heuristics in the gene redesign problem. For instance, the ability to return the best solutions constituting the main
part of the Pareto front, even in non-convex or non-continuous spaces. This allows a researcher to select synonymous
genes among the optimal solutions, to best suit his purpose, instead of accepting a single solution that might represent an
unwanted trade-off between the objectives.
Keywords: Genetic algorithms, multi-objective optimization, pareto front, simulated annealing, Synthetic gene design, Codon, optimal solutions, heterologous expression, G+C, GENETIC ALGORITHM, GC content.
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