Use of Modeling in different branches of human knowledge and activities

Modeling in biology.

The method of modeling in biology is a means to establish all the deeper and complex relationships between biological theory and experience.

In the last century, an experimental method in biology began to run into certain boundaries, and it turned out that a whole series of studies was impossible without modeling. If we dwell on some examples of the limitations of the field of application of an experiment in biology, then they will be basically as follows:

a) experiments can be conducted only on currently existing facilities (the impossibility of extending the experiment to the region of the past);

b) interference with biological systems is sometimes of such a nature that it is impossible to establish the reasons for the changes that have appeared (as a result of intervention or for other reasons)

c) some theoretically possible experiments are not feasible due to the low level of development of experimental equipment;

d) a large group of experiments related to human experimentation should be rejected for moral and ethical reasons.

But modeling is widely used in the field of biology, not only because it can replace the experiment. It has great independent significance, which is expressed, according to a number of authors , in a number of advantages:

1. using the simulation method on one set of data, you can develop a number of different models, interpret the phenomenon under study in different ways, and choose the most fruitful of them for theoretical interpretation.

2. in the process of building a model, you can make various additions to the hypothesis under study and get its simplification.

3. in the case of complex mathematical models, computers can be used.

4. the possibility of carrying out model experiments (the synthesis of Miller’s amino acids, model experiments on experimental animals).

All this clearly shows that modeling performs independent functions in biology and is becoming an increasingly necessary step in the process of creating a theory. However, modeling retains its heuristic value only when the limits of application of any model are taken into account. This is especially impressively shown by R. S. Karpinskaya on the minimal cell model. This model arose as a result of the knowledge of the biochemical universality of life and has a methodological significance for modeling its basic laws. The minimal cell is a model of the basic unit of life and covers only the membrane, reproduction system and the energy supply system. Thus, the task is to reproduce the most common life structures with its help.

And although the development aspect remains unaccounted for, the minimum cell model is of great importance for proving the unity of the organic world. However, this model does not go beyond the limits of the biochemical approach to life, which is mainly “aimed at proving its stable, universal and unchanging characteristics” . On the other hand, the minimal cell model can also be used to distinguish between certain qualitative steps of the development process. She, like any other model, has its own area of ​​applicability and allows you to recognize and reconstruct certain patterns. Thus, this model performs essential functions in the development of the theory.

For a deeper understanding of the meaning and essence of modeling in biology, one should dwell on the problems of modeling in the history of biological science.

Modeling as a scientific method in biology was first described and consciously used by Otto Büchii and Stefan Leduc in 1892 . From the point of view of the history of science, it is interesting that methods of modeling in biology began to be used consciously only when, thanks to the emergence of Darwin’s evolutionary theory and the creation of genetics in the development of biological theory, a major leap was made, and biology set about investigating ever more complex biotic relationships.

For example, the emergence of population genetics is closely related to the model of Hardy and Weinberg. Deep penetration into objective connections at the macro and micro levels of the living, as well as the transition to the study of superorganismic systems, led researchers to turn to the modeling method. All changes occurring in natural populations are of a very complex nature due to the interaction of many factors of evolution, so only the study of simpler models can give an idea of ​​the significance of individual evolutionary factors.

An important role was played by modeling in the development of molecular biology. One of the known examples of the application of modeling methods is the development of a structural model of DNA, which was created on the basis of X-ray structural analysis and chemical research, and was interpreted by Watson and Crick (1953). This model is particularly expressive of the relationship between experimental methods and modeling methods in the further development of biological theory. Issues related to the further application of modeling in molecular biology are widely considered in the work of the German researcher E. Thomas.


About cybernetic modeling and modeling of human mental activity.

Features of cybernetic modeling.

In modern scientific knowledge, the tendency of constructing cybernetic models of objects of various classes is quite widespread. “The cybernetic stage in the study of complex systems was marked by a significant transformation of the“ language of science, ”characterized by the possibility of expressing the main features of these systems in terms of information and control theory. This made their mathematical analysis available.” Cybernetic modeling is also used as a general heuristic tool, and as an artificial organism, and as a substitute system, and as a demonstration function. The use of the cybernetic theory of communication and control for constructing models in the respective areas is based on the maximum commonality of its laws and principles: for objects of living nature, social systems and technical systems.

The global use of cybernetic modeling allows us to consider this “logical-methodological” phenomenon as an integral element of the “intellectual climate” of modern science “. In this connection, they speak about a special” cybernetic thinking style “, about” cyberneticization “of scientific knowledge. With cybernetic modeling Possible directions for the growth of the theorizing processes of various sciences, an increase in the level of theoretical research, are discussed. Consider some examples of the inclusion of cybernetic ideas in others great conceptual systems.

Analysis of biological systems with the help of cybernetic modeling is usually associated with the need to explain some of the mechanisms of their functioning (we will see this below, considering the simulation of human mental activity). In this case, the system of cybernetic concepts and principles is the source of hypotheses regarding any self-governing systems, since ideas of connections and management are true for this field of application of ideas, new classes of factors.

Describing the process of cybernetic modeling , pay attention to the following circumstances. The model, being an analogue of the phenomenon under study, can never reach the degree of complexity of the latter. When building a model, they resort to well-known simplifications, the purpose of which is the desire to display not the whole object, but to characterize with a maximum of some of its “slice”. The task is to identify the properties that are important for the study by introducing a number of simplifying assumptions. Creating cybernetic models, allocate information and management properties. All other aspects of this object remain out of consideration. The extreme importance of finding ways to study complex systems by imposing certain simplifying assumptions is pointed out by R. Ashby. “In the past,” he notes, there was some neglect of simplifications … However, we who study complex systems cannot afford such neglect. Researchers of complex systems must deal with simplified forms, since comprehensive studies are often completely impossible. ”

Analyzing the process of application of cybernetic modeling in various fields of knowledge, one can notice the expansion of the scope of application of cybernetic models: use in the sciences of the brain, in sociology, in art, in a number of technical sciences. In particular, information models have found application in modern measuring equipment. The information theory of measurement and measuring devices arising from them is a new subsection of modern applied metrology.

In problems of various classes, the principle of feedback is used. In particular, Deutsch proposed a behavior motivation model based on this principle. This model allowed to clarify some mechanisms of animal behavior. According to Deutsch, training an animal in a maze consists not in working out a series of reactions, but in establishing the sequence of a number of sub-goals, the successive achievement of which leads to the final goal – the feeding trough. Here is not the training, but the regulation of already learned reactions. To explain this, Deutsch developed a hypothetical scheme based on a motivational model with feedback and also using the principles of common causal factors, chain reactions and inhibitory relationships.

The importance of the feedback principle is noted in the study of problems of biogeocenology by a number of researchers.


Modeling human mental activity.

Methods of classical physiology of higher nervous activity, morphophysiology, electrophysiology, biochemistry, etc. are important for brain research. However, the need has arisen for new methods that reveal brain activity from a different angle – from the point of view of regularities of management processes and information processing.

Attempts at systemic brain research are not new. Even N. M. Sechenov set the task of revealing the essence of the mechanism of brain activity by finding the underlying principles of this activity. He discovered one of them – the principle of reflexes.

IP Pavlov investigated the principles of controlling the dynamics of higher nerve centers, analyzing and synthesizing the signals coming from outside, and showed what the features of brain activity are in various states of the latter. The study of brain activity was also enriched by the studies of PK Anokhin.

As N. Kochergin notes, “for studying the brain as a complex functional system, the modeling method acquires great importance, making it possible to reveal the structure of the brain, the form of connections of neurons and different parts of the brain among themselves, the principles of neural organization, the patterns of processing, transmission, storage and coding information in the brain, etc. “The use of computers in the modeling of brain activity allows us to reflect the processes in their dynamics, but this method has its strengths and weaknesses in this application. Along with the common features inherent in the brain and the device modeling it to work, such as: – materiality – the regular nature of all processes – commonality of some forms of matter movement – reflection – belonging to a class of self-organizing dynamic systems in which are embedded:

a) feedback principle

b ) structural-functional analogy

c) the ability to accumulate information there are significant differences, such as:

1. only lower forms of movement — physical, chemical — are inherent in the modeling device, and the brain, in addition, is social, biological;

2. the process of reflection in the human brain is manifested in the subjective-conscious perception of external influences. Thinking occurs as a result of the interaction of the subject of knowledge with the object in the social environment;

3. in the language of man and machine. Human language is conceptual in nature.

Properties of objects and phenomena are summarized with the help of language. A simulator deals with electrical impulses, which are correlated by a person with letters, numbers. Thus, the machine “speaks” not in the conceptual language, but in the system of rules, which by its nature is formal, having no substantive content.

The use of mathematical methods in analyzing the processes of the reflective activity of the brain was made possible by certain assumptions formulated by McCulloch and Pitts. They are based on abstraction from the properties of a natural neuron, on the nature of the metabolism, etc. neuron is viewed from a purely functional side. Existing models that mimic brain activity (Furley, Clark, Neumann, Combertson, Walter, George, Shannon, Attlee, Berl, and others) are distracted from the qualitative specificity of natural neurons. However, from the point of view of studying the functional side of brain activity, this turns out to be insignificant.

In the literature there are a number of approaches to the study of brain activity: – the theory of automatic regulation (living systems are considered as a kind of ideal object) – informational (replaced the energy approach). Its main principles are:

a) the allocation of information links within the system

b) signal extraction from noise c) probabilistic nature

The successes obtained in studying the activity of the brain in the information aspect based on modeling, in the opinion of N. M. Amosov, created the illusion that the problem of patterns of brain functioning can be solved only with the help of this method. However, in his opinion, any model is associated with simplification, in particular: – not all functions and specific properties are taken into account – an abstraction from a social, neurodynamic character.

Thus, a conclusion is drawn about the critical attitude to this method (it is impossible to overestimate its capabilities, but at the same time, its widespread use in this area is necessary given reasonable limitations).


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