Результаты поиска по 'self-organization':
Найдено статей: 14
  1. Krasnyakov I.V., Bratsun D.A., Pismen L.M.
    Mathematical modeling of carcinoma growth with a dynamic change in the phenotype of cells
    Computer Research and Modeling, 2018, v. 10, no. 6, pp. 879-902

    In this paper, we proposed a two-dimensional chemo-mechanical model of the growth of invasive carcinoma in epithelial tissue. Each cell is modeled by an elastic polygon, changing its shape and size under the influence of pressure forces acting from the tissue. The average size and shape of the cells have been calibrated on the basis of experimental data. The model allows to describe the dynamic deformations in epithelial tissue as a collective evolution of cells interacting through the exchange of mechanical and chemical signals. The general direction of tumor growth is controlled by a pre-established linear gradient of nutrient concentration. Growth and deformation of the tissue occurs due to the mechanisms of cell division and intercalation. We assume that carcinoma has a heterogeneous structure made up of cells of different phenotypes that perform various functions in the tumor. The main parameter that determines the phenotype of a cell is the degree of its adhesion to the adjacent cells. Three main phenotypes of cancer cells are distinguished: the epithelial (E) phenotype is represented by internal tumor cells, the mesenchymal (M) phenotype is represented by single cells and the intermediate phenotype is represented by the frontal tumor cells. We assume also that the phenotype of each cell under certain conditions can change dynamically due to epithelial-mesenchymal (EM) and inverse (ME) transitions. As for normal cells, we define the main E-phenotype, which is represented by ordinary cells with strong adhesion to each other. In addition, the normal cells that are adjacent to the tumor undergo a forced EM-transition and form an M-phenotype of healthy cells. Numerical simulations have shown that, depending on the values of the control parameters as well as a combination of possible phenotypes of healthy and cancer cells, the evolution of the tumor can result in a variety of cancer structures reflecting the self-organization of tumor cells of different phenotypes. We compare the structures obtained numerically with the morphological structures revealed in clinical studies of breast carcinoma: trabecular, solid, tubular, alveolar and discrete tumor structures with ameboid migration. The possible scenario of morphogenesis for each structure is discussed. We describe also the metastatic process during which a single cancer cell of ameboid phenotype moves due to intercalation in healthy epithelial tissue, then divides and undergoes a ME transition with the appearance of a secondary tumor.

    Views (last year): 46.
  2. Kurakin P.V.
    Technoscape: multi-agent model for evolution of network of cities, joined by production and trade links
    Computer Research and Modeling, 2022, v. 14, no. 1, pp. 163-178

    The paper presents agent-based model for city formation named Technoscape which is both local and nonlocal. Technoscape can, to a certain degree, be also assumed as a model for emergence of global economy. The current version of the model implements very simple way of agents’ behavior and interaction, still the model provides rather interesting spatio-temporal patterns.

    Locality and non-locality mean here the spatial features of the way the agents interact with each other and with geographical space upon which the evolution takes place. Technoscape agent is some conventional artisan, family, or а producing and trading firm, while there is no difference between production and trade. Agents are located upon and move through bounded two-dimensional space divided into square cells. The model demonstrates processes of agents’ concentration in a small set of cells, which is interpreted as «city» formation. Agents are immortal, they don’t mutate and evolve, though this is interesting perspective for the evolution of the model itself.

    Technoscape provides some distinctively new type of self-organization. Partially, this type of selforganization resembles the behavior of segregation model by Thomas Shelling, still that model has evolution rules substantially different from Technoscape. In Shelling model there exist avalanches still simple equilibria exist if no new agents are added to the game board, while in Technoscape no such equilibria exist. At best, we can observe quasi-equilibrium, slowly changing global states.

    One non-trivial phenomenon Technoscape exhibits, which also contrasts to Shelling segregation model, is the ability of agents to concentrate in local cells (interpreted as cities) even explicitly and totally ignoring local interactions, using non-local interactions only.

    At the same time, while the agents tend to concentrate in large one-cell cities, large scale of such cities does not guarantee them from decay: there always exists a process of «enticement» of agents and their flow to new cities.

  3. Zenyuk D.A., Malinetsky G.G., Faller D.S.
    Simulation of corruption in hierarchical systems
    Computer Research and Modeling, 2014, v. 6, no. 2, pp. 321-329

    Simulation model of corruption in hierarchical systems which takes into account individual strategies of elements and collective behavior of large groups is proposed. Evolution of various characteristics like level of corruption or ratio of corrupted elements and their dependence on external parameters are discussed. The effectiveness of various anticorruptional strategies is examined by means of numeric analysis.

    Views (last year): 8. Citations: 11 (RSCI).
  4. Sergienko A.V., Akimenko S.S., Karpov A.A., Myshlyavtsev A.V.
    Influence of the simplest type of multiparticle interactions on the example of a lattice model of an adsorption layer
    Computer Research and Modeling, 2024, v. 16, no. 2, pp. 445-458

    Self-organization of molecules on a solid surface is one of the promising directions for materials generation with unique magnetic, electrical, and optical properties. They can be widely used in fields such as electronics, optoelectronics, catalysis, and biology. However, the structure and physicochemical properties of adsorbed molecules are influenced by many parameters that must be taken into account when studying the self-organization of molecules. Therefore, the experimental study of such materials is expensive, and quite often it is difficult for various reasons. In such situations, it is advisable to use the mathematical modeling. One of the parameters in the considered adsorption systems is the multiparticle interaction, which is often not taken into account in simulations due to the complexity of the calculations. In this paper, we evaluated the influence of multiparticle interactions on the total energy of the system using the transfer-matrix method and the Materials Studio software package. The model of monocentric adsorption with nearest interactions on a triangular lattice was taken as the basis. Phase diagrams in the ground state were constructed and a number of thermodynamic characteristics (coverage $\theta$, entropy $S$, susceptibility $\xi$) were calculated at nonzero temperatures. The formation of all four ordered structures (lattice gas with $\theta=0$, $(\sqrt{3} \times \sqrt{3}) R30^{\circ}$ with $\theta = \frac{1}{3}$, $(\sqrt{3} \times \sqrt{3})R^{*}30^{\circ}$ with $\theta = \frac{2}{3}$ and densest phase with $\theta = 1$) in a system with only pairwise interactions, and the absence of the phase  $(\sqrt{3}\times \sqrt{3}) R30^\circ$ when only three-body interactions are taken into account, were found. Using the example of an atomistic model of the trimesic acid adsorption layer by quantum mechanical methods we determined that in such a system the contribution of multiparticle interactions is 11.44% of the pair interactions energy. There are only quantitative differences at such values. The transition region from the  $(\sqrt{3} \times \sqrt{3}) R^{*}30^\circ$ to the densest phase shifts to the right by 38.25% at $\frac{\varepsilon}{RT} = 4$ and to the left by 23.46% at $\frac{\varepsilon}{RT} = −2$.

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