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

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Ekaterina M. Myasnikova¹, Alexander Spirov²
¹Peter the Great St. Petersburg Polytechnical University, ekmyasnikova@yandex.ru
²lab Modeling Evolution
I.M. Sechenov Institute of Evolutionary
Physiology and Biochemistry Russian
Academy of Sciences
Saint-Petersburg, Russia
alexander.spirov@gmail.com

The ideas of evolutionary biology used to inspire computer scientists to develop a thriving nowadays field of genetic algorithms (GA). Nowadays, the directed evolution of biological macromolecules is reasonably interpreted as an implementation of GA in biochemical experiments. One of the theoretical foundations of GA, justifying the effectiveness of evolutionary search, is the concept of building blocks (BB). In GA, it is reasonable to match these BBs to domains / motifs of macromolecules in biology. Computer scientists have shown the importance of identifying and preserving already found BBs for the effectiveness of evolutionary search. For this purpose, dozens of procedures and algorithms have been developed. On the other hand, the experimental procedures finding and preserving domains (motifs) remain a poorly developed area in the techniques of evolution in vitro. In this report, we demonstrate how significantly some heuristic algorithms preserving the BBs can increase the efficiency of in vitro evolution in numerical experiments. As a benchmark test, we use such actual problem of synthetic biology as the evolutionary search for multi-domain RNA devices. The success of these primary tests with simple heuristic algorithms gives us every reason to expect that the implementation and application of more advanced and modern GA procedures will give an even greater increase in efficiency. In conclusion, we discuss the importance of such highly efficient heuristic algorithms for evolutionary and synthetic biology.