## 1 Introduction
The issues of the development of mathematical models in medicine and experimental scientific works are actively considered in the modern scientific community. [PERSON]'s study [6] is devoted to a simple mathematical model of aging based on a system of autonomous ODEs of the first order. The key assumption of the model is that the organism has no internal clock counting down chronological time on the scale of decades. Instead, the organism uses internal biological factors denoted by variables \(g_{i}(t)\), each of which counts down its biological time \(t_{i}\). Overall, the proposed model provides a simple and efficient framework for systematizing experimental data on age-related changes in different biological subsystems of an organism. Even in linear approximation, the parameters \(\{a_{i},b_{ij}\}\) allow us to quantitatively identify and structure the significant cause-and-effect relationships for the aging process. [PERSON] et al. represent the aging process using a one-dimensional model of a multicellular system that allows the analysis of local and global modes of cell behavior [7]. In the early stages, simple interactions provide coordinated patterns, whereas in later stages, complex interactions result in chaotic changes reflecting an abnormal state. In the study [8], the complex cellular mechanisms that characterize aging are reviewed with a focus on two metabolic centers: mTOR and NAD+-dependent deacetylase SIRT1. Experimental evidence points to an interaction between these pathways, but the mechanisms of their interaction remain unclear. The authors propose to use computational modelling combined with experiments to elucidate these mechanisms. The basic models are discussed and a reduced reaction pathway for modelling is presented. Finally, the limitations of computational modelling and opportunities for future research in this area are described. The main limitation of the approach is the need for a precise set of parameters. Therefore, well-studied areas should be modelled first to gradually understand the mechanisms and develop new therapies to increase healthy life expectancy or slow aging. Moreover, in [9], a mathematical model describing the influence of the environment on the aging of living systems is presented. It is based on the competition between the destruction and restoration of a biological system regulated by the kinetics of autocatalytic reactions. The influence of the environment is considered through time-dependent parameters corresponding to thermodynamic and gerontological principles, as well as medical observations. [PERSON] [10] discusses mathematical models of physiological behavior that describe a system returning to the initial state after a perturbation through homeostasis. These models include the concept of a \"fatal limit\" of system deviation. The author concludes that the numerical values of the limits depend on other parameters, such as recovery times and coupling coefficients, and can be experimentally measured. Age-related changes in parameters and the derivation of mortality parameters, such as Gompertz parameters, from experimental data are discussed. The process of proliferative cell senescence in culture has been described using a mathematical model [11]. Based on the hypothesis of DNA damage as a cause of cell senescence, the model can explain both limited and unlimited proliferative potential of both normal and transformed cells in vitro. According to the model, the fate of a cell population depends on two counteracting factors: the rate of proliferation of dividing cells and the rate of accumulation of gene damage. Computer simulations demonstrate agreement with experimental data in general terms. Based on [PERSON]'s mathematical model, a 10-year cardiovascular disease (CVD) risk estimate using the WHO scale was presented in [12] using data from 5503 adults in Malaysia. It was found that 4.9% of participants had a high risk (\(\geq\)20%), which was more common in males (7.3%) compared to females (2.5%).