Transfer of Genetic Algorithms to Directed Evolution of Macromolecules: Tests in Silico

Abstract

Evolutionary computations is an impressive example of the convergence of two research fields: evolutionary biology and computer sciences. While initially genetic algorithms (GAs) were inspired by the ideas of the theory of evolution in biology, there is now a tendency toward the reverse transfer of ideas to a biological experiment. An example of such a transfer is the directed evolution of macromolecules that can be considered as an analogue of GAs in biochemical experiments. We focus on the concept of building blocks (BB) that underlies the theoretical and practical effectiveness of GA in evolutionary search. In in vitro experiments the modular structure of macromolecules points to the parallels between domains/motifs and BBs. In computer science there were developed a great number of algorithms for finding and preserving BBs. It was proved that such procedures provide the effectiveness of the evolutionary search. In this work we show how significantly some heuristic algorithms preserving the BBs can increase the efficiency of the in vitro evolution. As a benchmark test, we use such an actual problem of synthetic biology as the evolutionary search for multi-domain RNA devices. The results of these tests with simple heuristic algorithms are very promising with the prospective of further implementation of more advanced GA procedures in the evolutionary experiments in vitro. In conclusion, we discuss the importance of such highly efficient heuristic algorithms for the evolutionary and synthetic biology.