Heuristic algorithms in Evolutionary Computations and modular organization of biological macromolecules: applications to directed evolution
A while ago, the ideas of evolutionary biology inspired computer scientists to develop a thriving nowadays field of evolutionary computation (EC), in general, and genetic algorithms (GA), in particular. At the same time, the directed evolution of biological molecules (in vitro evolution) 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 EC, it is reasonable to match these BBs to domains and motifs of macromolecules in evolutionary and synthetic biology. Computer scientists have shown and carefully studied the importance of preserving already found BBs for the effectiveness of evolutionary search. For this purpose, dozens of algorithms have been developed, including heuristic crossover algorithms. On the other hand, the experimental procedures defining and preserving domains remain a poorly developed area in the techniques of evolution in vitro. In this paper, we demonstrate how several simple algorithms preserving the BBs can increase the efficiency of in vitro evolution in numerical experiments by almost an order of magnitude. As test problems, we propose and use such well-known problems of synthetic biology as the evolutionary search for strong bacterial promoters (with several motifs) and search for multi-domain RNA devices, as compared to the classic GA tests (Royal Road functions). The success of these primary tests with simple algorithms gives us every reason to expect that the implementation and application of more advanced and modern EC procedures will give an even greater increase in efficiency. Such an increase in search efficiency will significantly reduce the cost of in vitro evolution experiments, which will fully cover the costs of developing new experimental procedures based on these algorithms.