Heuristic algorithms in evolutionary computation and modular organization of biological macromolecules: Applications to in vitro evolution

Abstract

Evolutionary computing (EC) is an area of computer sciences and applied mathematics covering heuristic optimization algorithms inspired by evolution in Nature. EC extensively study all the variety of methods which were originally based on the principles of selectionism. As a result, many new algorithms and approaches, significantly more efficient than classical selectionist schemes, were found. This is especially true for some families of special problems. There are strong arguments to believe that EC approaches are quite suitable for modeling and numerical analysis of those methods of synthetic biology and biotechnology that are known as in vitro evolution. Therefore, it is natural to expect that the new algorithms and approaches developed in EC can be effectively applied in experiments on the directed evolution of biological macromolecules. According to the John Holland’s Schema theorem, the effective evolutionary search in genetic algorithms (GA) is provided by identifying short schemata of high fitness which in the further search recombine into the larger building blocks (BBs) with higher and higher fitness. The multimodularity of functional biological macromolecules and the preservation of already found modules in the evolutionary search have a clear analogy with the BBs in EC. It seems reasonable to try to transfer and introduce the methods of EC, preserving BBs and essentially accelerating the search, into experiments on in vitro evolution. We extend the key instrument of the Holland’s theory, the Royal Roads fitness function, to problems of the in vitro evolution (Biological Royal Staircase, BioRS, functions). The specific version of BioRS developed in this publication arises from the realities of experimental evolutionary search for (DNA-) RNA-devices (aptazymes). Our numerical tests showed that for problems with the BioRS functions, simple heuristic algorithms, which turned out to be very effective for preserving BBs in GA, can be very effective in in vitro evolution approaches. We are convinced that such algorithms can be implemented in modern methods of in vitro evolution to achieve significant savings in time and resources and a significant increase in the efficiency of evolutionary search.

Prospects for the Implementation of Heuristic Algorithms From the Evolutionary Computing as New Experimental Approaches in Synthetic Biology: In Silico Tests

“…Molecular devices are typically synthesized experimentally by methods of directed evolution (in vitro evolution; reviewed in Spirov, Myasnikova, 2022a;Spirov, Myasnikova, 2022b). In this approach, an initial "population" is formed from macromolecules, usually of a given length, completely or partially randomized in sequence.…”

“…It is costly and resource intensive and typically manipulates sequence libraries several orders of magnitude less than older approaches. This range of problems brings to the fore the search and testing of new approaches that have the potential of making evolutionary search more efficient (Spirov, Myasnikova, 2022a;Spirov and Myasnikova, 2022b).…”

“…Many experimental researchers in the field of in vitro evolution systematically emphasize the importance and promise of preliminary numerical tests of the problems that experimenters have to solve (e.g. Voigt et al, 2002;Komarova & Kuznetsov, 2019;Spirov, Myasnikova, 2022a;Spirov, Myasnikova, 2022b). So, in silico tests should precede costly and resource-intensive real experiments, determining the choice of specific methods, the choice of parameters, as well as an assessment of the chances of success.…”

“…It is important that the field of evolutionary computations is a source of those methods and algorithms that could be used as numerical models of new experimental approaches in microdroplet microfluidics (Spirov, Myasnikova, 2022a;Spirov, Myasnikova, 2022b).…”

Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing

“…Most of them are swarm intelligence algorithms that imitate natural bodies, such as firefly algorithm, ant colony algorithm, and bee colony algorithm. They are applied to solve various optimization scheduling problems and are widely used in industry [ 6 ], network transmission [ 7 ], biology [ 8 ] and cloud computing [ 9 ]. Among them, the combination of neural networks and other algorithms has achieved considerable research success in image capture and retrieval [ 10 , 11 , 12 , 13 ].…”