In order to participate in the seminar, on September 17 after 4:45 pm (UTC+7) you should connect to the Zoom conference via the following link https://us02web.zoom.us/j/89776462466?pwd=WFBrZFJDTDdzNUtUN1VEeFhHREpmQT09 or manually using the Zoom conference ID 897 7646 2466 and password 549526. If you have problems logging into Zoom, you can connect to the YouTube livestream of the seminar https://youtube.com/channel/UCEfDHH6-AcdAYEaizUzrelw
Adaptation to changing external conditions is the basis of the evolutionary process. The report examines a mathematical model of evolutionary adaptation of replicator systems, which describe the quantitative and qualitative characteristics of a community of biological organisms. The dynamics of these systems is determined by solutions of systems of nonlinear ODEs of sufficiently large dimensions. The main hypothesis of the proposed model is the assumption that the time of evolutionary adaptation of the fitness landscape (a set of parameters that determine the dynamics of the system) is many times slower than the time of active dynamics of the system (fast time of active dynamics, slow time of adaptation). Another important postulate of this theory is based on the statement of the fundamental theorem on natural selection by R. Fisher that any biological system in the process of evolution tends to increase the value of average fitness (fitness). Examples of evolutionary adaptation of specific systems will be given. It has been proven that as a result of the process of evolutionary adaptation, systems become stable (resistant) in relation to parasitic macromolecules and microorganisms, from the influence of which they died until the moment of evolutionary change. The problems of evolutionary adaptation of the fitness landscape with changes in the mortality rates of species are considered. It is shown that with the purposeful destruction of the so-called main species, other species gain an advantage in evolutionary development in the course of therapy. These results make it possible to predict the response of systems to changes in mortality rates and have practical applications in the treatment of malignant cells and pathogenic bacteria.