Self-organization Mechanism Research Articles

Spatial–temporal order–disorder transition in angiogenic NOTCH signaling controls cell fate specification

Angiogenesis is a morphogenic process resulting in the formation of new blood vessels from pre-existing ones, usually in hypoxic micro-environments. The initial steps of angiogenesis depend on robust differentiation of oligopotent endothelial cells into the Tip and Stalk phenotypic cell fates, controlled by NOTCH-dependent cell–cell communication. The dynamics of spatial patterning of this cell fate specification are only partially understood. Here, by combining a controlled experimental angiogenesis model with mathematical and computational analyses, we find that the regular spatial Tip–Stalk cell patterning can undergo an order–disorder transition at a relatively high input level of a pro-angiogenic factor VEGF. The resulting differentiation is robust but temporally unstable for most cells, with only a subset of presumptive Tip cells leading sprout extensions. We further find that sprouts form in a manner maximizing their mutual distance, consistent with a Turing-like model that may depend on local enrichment and depletion of fibronectin. Together, our data suggest that NOTCH signaling mediates a robust way of cell differentiation enabling but not instructing subsequent steps in angiogenic morphogenesis, which may require additional cues and self-organization mechanisms. This analysis can assist in further understanding of cell plasticity underlying angiogenesis and other complex morphogenic processes.

Spatial-temporal order-disorder transition in angiogenic NOTCH signaling controls cell fate specification.

Angiogenesis is a morphogenic process resulting in the formation of new blood vessels from pre-existing ones, usually in hypoxic micro-environments. The initial steps of angiogenesis depend on robust differentiation of oligopotent endothelial cells into the Tip and Stalk phenotypic cell fates, controlled by NOTCH-dependent cell-cell communication. The dynamics of spatial patterning of this cell fate specification are only partially understood. Here, by combining a controlled experimental angiogenesis model with mathematical and computational analyses, we find that the regular spatial Tip-Stalk cell patterning can undergo an order-disorder transition at a relatively high input level of a pro-angiogenic factor VEGF. The resulting differentiation is robust but temporally unstable for most cells, with only a subset of presumptive Tip cells leading sprout extensions. We further find that sprouts form in a manner maximizing their mutual distance, consistent with a Turing-like model that may depend on local enrichment and depletion of fibronectin. Together, our data suggest that NOTCH signaling mediates a robust way of cell differentiation enabling but not instructing subsequent steps in angiogenic morphogenesis, which may require additional cues and self-organization mechanisms. This analysis can assist in further understanding of cell plasticity underlying angiogenesis and other complex morphogenic processes.

Dual-color live imaging unveils stepwise organization of multiple basal body arrays by cytoskeletons

For mucociliary clearance of pathogens, tracheal multiciliated epithelial cells (MCCs) organize coordinated beating of cilia, which originate from basal bodies (BBs) with basal feet (BFs) on one side. To clarify the self-organizing mechanism of coordinated intracellular BB-arrays composed of a well-ordered BB-alignment and unidirectional BB-orientation, determined by the direction of BB to BF, we generated double transgenic mice with GFP-centrin2-labeled BBs and mRuby3-Cep128-labeled BFs for long-term, high-resolution, dual-color live-cell imaging in primary-cultured tracheal MCCs. At early timepoints of MCC differentiation, BB-orientation and BB-local alignment antecedently coordinated in an apical microtubule-dependent manner. Later during MCC differentiation, fluctuations in BB-orientation were restricted, and locally aligned BB-arrays were further coordinated to align across the entire cell (BB-global alignment), mainly in an apical intermediate-sized filament-lattice-dependent manner. Thus, the high coordination of the BB-array was established for efficient mucociliary clearance as the primary defense against pathogen infection, identifying apical cytoskeletons as potential therapeutic targets.

What can we learn from a master plot of energy rate versus mass for a very wide variety of (complex) systems?

Mass and energy rate (ER) data have been collected for a wide variety of (complex) systems from the biological, cultural, and cosmological realms. They range from the cytochrome oxidase protein (10-22 kg and 6x10-19 W) to the observable universe (1.5x1053 kg and 1048 W) and, thus, span 75 mass and 66 ER orders of magnitude. Many of these systems are relevant for the big history (BH) narrative, i.e., the development of complexity over “big time” from the Big Bang up to the human society on Earth of today. The purpose of this paper is not per se to describe their history though, but to explore a master plot of ER vs. mass. Notably, the development of systems over big time has followed a rather tortuous path criss-crossing over this ER vs. mass master plot. The true mass of the system as a whole is used (for example trees including the non-living wood, living organisms including their intrinsic water, and social systems including the built constructs), because these inactive parts are essential for the performance of the system and facilitate its ER. A double logarithmic master plot of all ER vs. mass data shows clusters of data points. To some extent, this provides quantitative support for the distinction between the (sub-)realms, which is based on a qualitative description of their material structure and energy processing. In the master plot, small systems with low mass and ER converge into larger systems with larger mass and ER, which is typically accompanied by a decrease of the energy rate density (ERD = ER/mass). Correlation of ER with mass for various groups of systems demonstrates both sub- and supra-linear scaling with the power law β constant varying between 0.5 and 4.0, showing that the mechanisms of self-organisation are quite different for the corresponding system groups. The combination of convergence and scaling with β always larger than zero explains why the ER & mass data points fall in a diagonal band with a width of 17 orders of magnitude.
 ER and mass have changed over wide ranges during the evolution of groups of systems, suggesting that evolution can be viewed as a process of systems exploring a larger ER vs. mass area until they run into ER and/or mass limitations. Indeed, there is a diagonal ER vs. mass limit for stable systems in all realms, corresponding to an ERD value of around 105 W/kg. Systems with ER & mass combinations above this limit, such as bombs, super-novae and cosmological transients, are unstable and “explosive”. This raises the interesting question of whether such an ERD maximum puts a limit on the development of complexity over big time. It seems that the low, right side of the master plot is empty. However, it is argued here that it is full of systems with low ER, such as dormant, living organisms, technological systems with their power adjusted or even switched off, as well as cooling, cosmological objects. Such systems are typically considered of less interest in a BH context, but they are viewed here as simple, complex systems which are out of equilibrium with matter, energy and information stored in their structure. While ERD appears to increase with the ‘advancement’ of systems over big time [5,51,52], there are quite some confounding factors regarding the efficacy of ERD as a metric for complexity in BH. For example, ERD decreases during the lifetime of a human and the human society (the mass of human-made constructs has grown faster than the global energy consumption), as well as during the evolution of living organisms and stars, whereas complexity is considered to increase. High ERD values of system parts may be illustrative for the complexity of the larger system, but are not representative for ERD of the system itself. Machines with an increased efficiency of energy conversion have a lower ERD, but could be considered more complex. The smallest and largest ERD values observed for the various realms appear to correlate with activity level and reciprocally with size, which do not per se reflect complexity. It is hoped that the raw data collected and the major trends observed in this paper will offer new insights into various aspects of the evolution of the universe over big time, and serve as an important resource for other related studies.

Thermo-inspired model of self-propelled hard disk agents for heterogeneous bidirectional pedestrian flow

The aim of this work is to present a hybrid pedestrian model that describes the bidirectional walking of crowds in a corridor. The self-propelled hard disk model combines both perceptual as well as decision-making characteristics of situated agents. This model considers homogeneous and heterogeneous groups with relaxed, standard, and hurried pedestrian profiles according to their walking preferences. The situated agent perception module considers one radius in the base model, while the extended model considers three radii to improve the pedestrian evasion rules. We have explored the corresponding changes in the formation of spatial walking patterns. The findings include comparative variations in the formation of following lanes, the time-based evolution of crowds in both homogeneous and heterogeneous groups, and the transition from transient traffic jams to the appearance of segregation waves emerging from self-organization mechanisms. In this point, we propose a thermodynamics-inspired model associated with the collective behavior of pedestrians. A macroscopic ideal gas-type equation of state is established to define the dependence between volume, pressure, and temperature of pedestrian crowds in counterflow. To achieve this goal, the temperature, defined as T∼vα¯ where v is the walking speed of individual pedestrians, must possess an α parameter in the interval [1,2]. The experimental simulations include binary contact interactions and collisions of pedestrians with the corridor borders. The macroscopic description of the model inspired by thermodynamics can be complemented with the usual analysis of the fundamental diagram.

Self-organized BMP signaling dynamics underlie the development and evolution of digit segmentation patterns in birds and mammals.

Repeating patterns of synovial joints are a highly conserved feature of articulated digits, with variations in joint number and location resulting in diverse digit morphologies and limb functions across the tetrapod clade. During the development of the amniote limb, joints form iteratively within the growing digit ray, as a population of distal progenitors alternately specifies joint and phalanx cell fates to segment the digit into distinct elements. While numerous molecular pathways have been implicated in this fate choice, it remains unclear how they give rise to a repeating pattern. Here, using single-cell RNA sequencing and spatial gene expression profiling, we investigate the transcriptional dynamics of interphalangeal joint specification in vivo. Combined with mathematical modeling, we predict that interactions within the BMP signaling pathway-between the ligand GDF5, the inhibitor NOGGIN, and the intracellular effector pSMAD-result in a self-organizing Turing system that forms periodic joint patterns. Our model is able to recapitulate the spatiotemporal gene expression dynamics observed in vivo, as well as phenocopy digit malformations caused by BMP pathway perturbations. By contrasting in silico simulations with in vivo morphometrics of two morphologically distinct digits, we show how changes in signaling parameters and growth dynamics can result in variations in the size and number of phalanges. Together, our results reveal a self-organizing mechanism that underpins amniote digit segmentation and its evolvability and, more broadly, illustrate how Turing systems based on a single molecular pathway may generate complex repetitive patterns in a wide variety of organisms.

A networking view of collaborative consumption on social media: integrating value-in-exchange and value-in-use into value co-creation

PurposeThis study focuses on the self-organized cooperative consumption of platform participants on social media platform, and reveals how the brand owner cooperates with two-sided customers to achieve value co-creation.Design/methodology/approachThe authors adopted a case study approach to explore how a Chinese beauty startup developed collaborative networks from 2013 to 2022, and tracked the the changes of network structure and cooperation mechanism.FindingsThe study finds that the brand owner cooperates with two-sided customers to integrate resources and establish diverse relational trust, which enhances the evolution of a heterogeneous collaborative network for value co-creation.Originality/valueThe study builds upon traditional dyadic actor-to-actor interactions between providers and customers, develops a novel interaction framework of actor-to-network to explain the value co-creation by collaborative networking, reveals the self-organized mechanism of cooperative consumption on social media.

Cooperative Event-Triggered Fuzzy-Neural Multivariable Control With Multitask Learning for Municipal Solid Waste Incineration Process

Municipal solid waste incineration (MSWI) is an industrial process with multiple mechanism reactions, which has strong coupling and time-varying dynamics. It is extremely difficult to design a feasible multivariable controller for MSWI process due to the complex composition of municipal solid waste and fluctuation of calorific value. To solve these problems, a cooperative event-triggered fuzzy-neural multivariable controller with multi-task learning (CETFNMC-MTL) is proposed to realize the adaptive multivariable control of MSWI process. First, a fuzzy-neural multivariable controller is established to control furnace temperature and oxygen content synchronously. Second, a dynamic self-organizing mechanism based on multi-task learning is designed, which splits and merges neurons by calculating the dynamic time warping distance and cumulative contribution of neurons in the continuous time. Third, a cooperative event-triggered mechanism is introduced to improve controller update efficiency while reducing mechanical wear and computational burden. Then, the stability of parameters learning and structure self-organizing process is analyzed to guarantee the successful application of CETFNMC-MTL. Finally, the effectiveness of the controller is tested with process data from an MSWI plant in Beijing, China. The results show that the proposed CETFNMC-MTL has adaptive learning ability, while reducing energy consumption and improving control accuracy.

Twitter Self-Organization to the Edge of a Phase Transition: Discrete-Time Model and Effective Early Warning Signals in Phase Space

Many real-world systems of various origins are capable of self-organization to the edge of a phase transition, characterized by avalanche-like behavior. Therefore, it is important, by observing the behavior of early warning measures for dynamical series generated by systems, to timely see the early warning signals (precursors) of such self-organization and, if necessary, take preventive measures. To date, convincing evidence of self-organization to the edge of a phase transition has been obtained, but no effective precursors for this self-organization have been found. This research explores precursors for the Twitter self-organization based on the analysis of the behavior of measures directly related to the critical slowdown of the network and measures of the phase space reconstructed by the Takens method for the series of the number of network users creating avalanches of retweets in the network, corresponding to the three debates of the 2016 United States Presidential Election. We hydrated the relevant Tweet IDs, which were obtained from the Harvard Dataverse using the Social Feed Manager, to form this series. Preliminarily, we explore the potential of measures for early detection of self-organization of sandpile cellular automata as systems with Twitter-equivalent self-organization mechanisms. The equivalence is justified in the proposed discrete-time model for Twitter self-organization to the edge of a phase transition. It is found that there are more moments of the Twitter self-organization than the moments of time when debates started, and Twitter stays at the edge of a phase transition longer than the debate lasts. The effective measures, as the measures with the lowest number of false early warning signals, among all studied measures and for all studied systems, are dispersion and correlation dimension. Obtained results are practically important in the design and implementation of early warning systems for the systems with similar mechanisms for sandpile cellular automata self-organization to the edge of a phase transition.

Mesoscale numerical simulation of the multiple step reaction in hydrogen reduction of iron oxides

This work designed a mesoscale method of multi-step reduction of Fe2O3 with H2 based on Lattice Gas Cellular Automata (LGCA). Particles in the LGCA were marked to distinguish all the reactants, intermediates, products, and inert. A self-organized evolutionary mechanism is designed. The hydrogen reduction of Fe2O3 in the temperature range of 460–550 °C and 650–800 °C was simulated. Results show that the intermediate product Fe3O4 was found in both conditions, and its mass fraction quickly reach a peak at ∼0.8 and gradually decrease thereafter. An “induction period” can be identified in the initial stage of the reaction at 650–800 °C, where the reduction rate is slow due to the hindered formation of FeO. The porosity inside the Fe2O3 particles increases continuously through the reduction process, resulting in significant changes in the flow field around the particles. Consequently, the wake vortex gradually decreases and disappears eventually.

The Dynamics of Interorganizational Collaboration in Disaster Management: A Network Study Based on Flood Disasters in China

Interorganizational collaboration networks have become an important tool for disaster management. However, research on how different organizations can effectively collaborate throughout the entire disaster management process in centralized states such as China is scarce. This study begins to fill this lacuna by investigating interorganizational collaboration in different phases of disaster management and analyzing changes in the structure of the networks constructed during the preparedness and response phases of the 2020 flood disaster in Hubei Province, China. Building on the complex adaptive systems (CAS) theory, we argue that interorganizational collaboration changes dynamically according to its tasks and requirements. In the preparedness phase, interorganizational collaborations primarily follow established plans and choose horizontal self-organized collaboration mechanisms. However, when the urgent information and resource requirements increase in the response phase, many organizations choose vertical mandatory collaboration mechanisms. We found that organizations at the central and provincial levels in China were well positioned to coordinate information and resources and strengthen the interorganizational collaboration and communication that is crucial in disaster management. These findings contribute to the study of interorganizational collaboration networks in disaster management.

Self-Organizing Democratized Learning: Toward Large-Scale Distributed Learning Systems.

Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems toward large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to study a generalization in distributed learning systems that go beyond existing mechanisms such as federated learning (FL). Moreover, such learning systems rely on hierarchical self-organization of well-connected distributed learning agents who have limited and highly personalized data and can evolve and regulate themselves based on the underlying duality of specialized and generalized processes. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this article. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, hierarchical generalized learning problems in recursive forms are formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical update mechanisms. To that end, a distributed learning algorithm, namely DemLearn, is proposed. Extensive experiments on benchmark MNIST, Fashion-MNIST, FE-MNIST, and CIFAR-10 datasets show that the proposed algorithm demonstrates better results in the generalization performance of learning models in agents compared to the conventional FL algorithms. The detailed analysis provides useful observations to further handle both the generalization and specialization performance of the learning models in Dem-AI systems.

EVOLUTIONARY-SYNERGETIC APPROACH TO THE PROBLEM OF SOCIAL SUSTAINABILITY

Relevance. The social dimension of the concept of sustainable development is recognized as the most relevant but least developed. Ensuring social sustainability requires effective interdisciplinary cooperation based on a modern evolutionary-synergetic picture of the world.
 Objective. To formulate the concept of social system sustainability within the framework of evolutionary-synergetic paradigm.
 Materials and Methods. The philosophical and methodological analysis of the works of foreign and domestic authors in the field of social philosophy, sociology and synergetics was carried out.
 Results. The notions of sustainability in synthetic evolution of G. Spencer are considered. The role of division of labor in ensuring the social sustainability from the position of the concept of social solidarity of E. Durkheim is revealed. The mechanism of ensuring the sustainability of civilizations in the theory of historical evolution A. Toynbee is described. The prevailing role of sociocultural metaevolution in increasing of adaptive sustainability of society is noted. The paper analyzes the principles of the antifragile strategy proposed by N.N. Taleb to increase sustainability in conditions of nonlinearity and uncertainty.The applicability of synergetic methodology to the study of the problem of social sustainability is substantiated. The role of personality in the formation of a sustainable social system in the synergetic concept of S.P. Kurdyumov and E.N. Knyazeva is established.
 Discussion. The problem of the balance between chaos and order in sustainable society, as well as the mechanisms of self-organization of the social system in the studies of the Russian school of synergetics is considered.
 Conclusion. From the standpoint of evolutionism and synergetics, the sustainability of society is a property of purposeful increase in the efficiency of social processes as it moves towards the at-tractor of social justice along the optimal trajectory with minimal controlling influence.

The spatial pattern of Scirpus mariqueter expansion and the associated mechanism of self-organization using unmanned aerial vehicles and its significance for coastal wetland restoration.

Understanding the spatial expansion process of salt marshes and quantifying the factors driving this expansion are crucial for the management and restoration of coastal wetlands. In this study, we aimed to illustrate the expansion process of Scirpus mariqueter using drone remote sensing and quantify its relationship with habitat quality. Our hypothesis was that landscape metrics could serve as valuable indicators for prioritizing habitat restoration efforts along the coast. We utilized drone remote sensing and adopted the simple Greenness Index to reflect the growth status of S. mariqueter. Using this index, we computed the standard deviation ellipse and growth center. To evaluate habitat quality, we developed a method based on our previous research and other relevant reports. We then conducted a quantitative analysis of the expansion process of S. mariqueter in areas with varying habitat quality. We found that S. mariqueter's optimal elevation was 3.7m, with a range of 2.5 to 4.3m. The threshold value for soil total nitrogen was 0.3g/kg, and the tolerance threshold for soil salinity was 2500ppm. These three factors, elevation, soil total nitrogen, and soil salinity, collectively influenced habitat quality, with weights of 0.68, 0.23, and 0.09, respectively, as determined through geodetector analysis. During the summer, we observed a dominance of dispersal in S. mariqueter, with the species primarily spreading to areas with increased habitat quality. Patch shapes tended to be compact and regular in this season. In contrast, during the autumn, a dominance of decline was observed, with S. mariqueter mainly distributing to areas exhibiting decreased habitat quality. Patch shapes tended to be complex and irregular in the autumn season. Eventually micro-geomorphic modification and patch shape filling methods based on UAV observations are proposed to aid wetland restoration. These findings are of utmost importance for the restoration of coastal wetlands and the enhancement of ecosystem resilience.

Optimal wave reflection as a mechanism for seagrass self-organization

Ecosystems threatened by climate change can boost their resilience by developing spatial patterns. Spatially regular patterns in wave-exposed seagrass meadows are attributed to self-organization, yet underlying mechanisms are not well understood. Here, we show that these patterns could emerge from feedbacks between wave reflection and seagrass-induced bedform growth. We derive a theoretical model for surface waves propagating over a growing seagrass bed. Wave-induced bed shear stress shapes bedforms which, in turn, trigger wave reflection. Numerical simulations show seagrass pattern development once wave forcing exceeds a critical amplitude. In line with Mediterranean Sea field observations, these patterns have half the wavelength of the forcing waves. Our results raise the hypothesis that pattern formation optimizes the potential of seagrass meadows to reflect wave energy, and a clear direction for future field campaigns. If wave-reflecting pattern formation increases ecosystem resilience under globally intensifying wave climates, these ecosystems may inspire nature-based coastal protection measures.

Managing non-cooperative behaviors and ordinal consensus through a self-organized mechanism in multi-attribute group decision making

In consensus-based multi-attribute group decision making (MAGDM) problems, decision makers (DMs) may exhibit non-cooperative behaviors since they usually have different individual interests (sometimes conflicting) or limited knowledge, which strongly affects the efficiency of consensus and the quality of decision outcomes. Additionally, the existing MAGDM studies mainly focus on the cardinal consensus, and the ordinal consensus is ignored. Thus, this paper proposes a self-organized mechanism based framework to manage non-cooperative behaviors and ordinal consensus in MAGDM. First, a dual-membership function based on the basic idea of synergy theory is designed to detect non-cooperative behaviors at the element level of the multiple attribute evaluation matrix (MAEM), and then the weights of elements with non-cooperative behaviors are penalized automatically. In this way, the negative effects of non-cooperative behaviors can be eliminated. Next, a novel preference ranking-based ordinal consensus approach is proposed, which calculates an ordinal consensus based on the preference rankings of alternatives between individuals and the group. If the pre-defined consensus level is not reached, the feedback adjustment is used to help DMs modify their MAEMs to improve the consensus level; otherwise, the selection process is utilized to choose the optimal alternative(s). Finally, detailed simulation experiments and comparative analysis are designed to show the properties and effectiveness of the proposed framework, and an illustrative angel investment case is presented to demonstrate the calculation process and usability.

Self-organized periodic pattern dynamics of porous copper electrodeposition at the industrial large current

The formation of porous structure during metal electrodeposition not only provide new ideas for preparing advanced metal materials, but also determines the product defects in industrial electrodeposition processes. However, as for new evidence of ordered sub-structure formation within porous metal even during scalable templateless electrodeposition, existing thermodynamic reasons or exogenous-templates mechanism are under extensively controversial. Herein, we have discovered periodic self-organization behavior of porous copper at the initial stage of industrial large-current electrodeposition. Different from widely-recognized bubble-templated mechanism, the length of periodicity for Cu deposits is orders of magnitude smaller than any possible bubble template, according to in-situ observation. Coloration-tracking and in-situ electrochemical analysis experiments have been designed, to reveal the existence of a micelle-type complex intermediate and its activator effects. A self-organization mechanism has been proposed based on ordered dynamic pattern of micelle-type complex intermediate, rather than intermediate of small ions, which determines the porous structure since the very initial layer. Our findings suggest the important contribution from intrinsic self-organization dynamics of intermediate to the formation of porous metal materials with high activity or surface area in industrial scale.

Reinforcement learning swarm of self-organizing unmanned surface vehicles with unavailable dynamics

In this paper, self-organizing a swarm of unmanned surface vehicles (USVs) is creatively solved by optimal reinforcement learning control approach without acquiring USV dynamics. Self-organizing swarm mechanism for flexible decision-making is established by combing control theory with repulsive potentials, thereby lifting orientation constraints on USV swarm coordination. Distributed finite-time extended state observers are devised to sufficiently accommodate multiple unknowns covering internal dynamics and external uncertainties/disturbances. Swarming coordination of individual USV is optimally synthesized by reinforcement learning-based distributed control architecture. As a consequence, semi-global asymptotic stability of the entire self-organizing swarm can be rigorously ensured by Lyapunov analysis. Virtual-reality experiments show that reinforcement learning swarm is skilled in coordinating a herd of USVs tracking a trajectory.

Redundancy and the role of protein copy numbers in the cell polarization machinery of budding yeast

How can a self-organized cellular function evolve, adapt to perturbations, and acquire new sub-functions? To make progress in answering these basic questions of evolutionary cell biology, we analyze, as a concrete example, the cell polarity machinery of Saccharomyces cerevisiae. This cellular module exhibits an intriguing resilience: it remains operational under genetic perturbations and recovers quickly and reproducibly from the deletion of one of its key components. Using a combination of modeling, conceptual theory, and experiments, we propose that multiple, redundant self-organization mechanisms coexist within the protein network underlying cell polarization and are responsible for the module’s resilience and adaptability. Based on our mechanistic understanding of polarity establishment, we hypothesize that scaffold proteins, by introducing new connections in the existing network, can increase the redundancy of mechanisms and thus increase the evolvability of other network components. Moreover, our work gives a perspective on how a complex, redundant cellular module might have evolved from a more rudimental ancestral form.

Functional Model of Credit System

The subject of the research is the process of credit functioning, its goal is to develop a functional model of credit system. The research hypothesis assumes that credit continuous development ensures its adaptation to the changing environment. Methodology of the research was made up by the universal academic method, i. e. the outline of functional system, the method of analogies and critical analysis of literature. Within the elaborated functional model of the credit system components of credit were identified: store unit, two units of subsystem and resources providing its adaptation to the impact of the environment; mechanisms of organization and self-organization were substantiated; ways of transforming credit functional system in response to environment changes were demonstrated. The elaborated functional model of the credit system provides systematic comprehension of the process of credit functioning; explains the character of outward impact on the credit system and resources ensuring response to this impact; elucidates processes of organization and self-organization in the credit system. The obtained results can contribute to the development of academic theory of credit, give extra opportunities for grounding the objective regularities of its continuous development and propose additional ways for developing mechanisms of credit management.