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The paper outlines the approaches to mathematical modeling correlation adaptometry techniques widely used in biology and medicine. The analysis is based on models employed in descriptions of structured biological systems. It is assumed that the distribution density of the biological population numbers satisfies the equation of Kolmogorov-Fokker-Planck. Using this technique evaluated the effectiveness of treatment of patients with obesity. All patients depending on the obesity degree and the comorbidity nature were divided into three groups. Shows a decrease in weight of the correlation graph computed from the measured in the patients of the indicators that characterizes the effectiveness of the treatment for all studied groups. This technique was also used to assess the intensity of the training loads in academic rowing three age groups. It was shown that with the highest voltage worked with athletes for youth group. Also, using the technique of correlation adaptometry evaluated the effectiveness of the treatment of hormone replacement therapy in women. All the patients depending on the assigned drug were divided into four groups. In the standard analysis of the dynamics of mean values of indicators, it was shown that in the course of the treatment were observed normalization of the averages for all groups of patients. However, using the technique of correlation adaptometry it was found that during the first six months the weight of the correlation graph was decreasing and during the second six months the weight increased for all study groups. This indicates the excessive length of the annual course of hormone replacement therapy and the practicality of transition to a semiannual rate.

This is a brief review of the subjects, and an impression of some talks, which were given at the Schools on modelling complex biological systems. Those Schools reflected a logical progress in this way of thinking in our country and provided a place for collective “brain-storming” inspired by prominent scientists of the last century, such as A. A. Lyapunov, N. V. Timofeeff-Ressovsky, A. M. Molchanov. At the Schools, general issues of methodology of mathematical modeling in biology and ecology were raised in the form of heated debates, the fundamental principles for how the structure of matter is organized and how complex biological systems function and evolve were discussed. The Schools served as an important sample of interdisciplinary actions by the scientists of distinct perceptions of the World, or distinct approaches and modes to reach the boundaries of the Unknown, rather than of different specializations. What was bringing together the mathematicians and biologists attending the Schools was the common understanding that the alliance should be fruitful. Reported in the issues of School proceedings, the presentations, discussions, and reflections have not yet lost their relevance so far and might serve as certain guidance for the new generation of scientists.

Course “Natural models of parallel computing”, given for senior students of the Faculty of Computational Mathematics and Cybernetics, Moscow State University, is devoted to the issues of supercomputer implementation of natural computational models and is, in fact, an introduction to the theory of natural computing, a relatively new branch of science, formed at the intersection of mathematics, computer science and natural sciences (especially biology). Topics of the natural computing include both already classic subjects such as cellular automata, and relatively new, introduced in the last 10–20 years, such as swarm intelligence. Despite its biological origin, all these models are widely applied in the fields related to computer data processing. Research in the field of natural computing is closely related to issues and technology of parallel computing. Presentation of theoretical material of the course is accompanied by a consideration of the possible schemes for parallel computing, in the practical part of the course it is supposed to perform by the students a software implementation using MPI technology and numerical experiments to investigate the effectiveness of the chosen schemes of parallel computing.