74,958 research outputs found

    Adaptive Ising Model and Bacterial Chemotactic Receptor Network

    Full text link
    We present a so-called adaptive Ising model (AIM) to provide a unifying explanation for sensitivity and perfect adaptation in bacterial chemotactic signalling, based on coupling among receptor dimers. In an AIM, an external field, representing ligand binding, is randomly applied to a fraction of spins, representing the states of the receptor dimers, and there is a delayed negative feedback from the spin value on the local field. This model is solved in an adiabatic approach. If the feedback is slow and weak enough, as indeed in chemotactic signalling, the system evolves through quasi-equilibrium states and the ``magnetization'', representing the signal, always attenuates towards zero and is always sensitive to a subsequent stimulus.Comment: revtex, final version to appear in Europhysics Letter

    Control-data separation architecture for cellular radio access networks: a survey and outlook

    Get PDF
    Conventional cellular systems are designed to ensure ubiquitous coverage with an always present wireless channel irrespective of the spatial and temporal demand of service. This approach raises several problems due to the tight coupling between network and data access points, as well as the paradigm shift towards data-oriented services, heterogeneous deployments and network densification. A logical separation between control and data planes is seen as a promising solution that could overcome these issues, by providing data services under the umbrella of a coverage layer. This article presents a holistic survey of existing literature on the control-data separation architecture (CDSA) for cellular radio access networks. As a starting point, we discuss the fundamentals, concepts, and general structure of the CDSA. Then, we point out limitations of the conventional architecture in futuristic deployment scenarios. In addition, we present and critically discuss the work that has been done to investigate potential benefits of the CDSA, as well as its technical challenges and enabling technologies. Finally, an overview of standardisation proposals related to this research vision is provided

    A survey of self organisation in future cellular networks

    Get PDF
    This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

    A methodological approach to BISDN signalling performance

    Get PDF
    Sophisticated signalling protocols are required to properly handle the complex multimedia, multiparty services supported by the forthcoming BISDN. The implementation feasibility of these protocols should be evaluated during their design phase, so that possible performance bottlenecks are identified and removed. In this paper we present a methodology for evaluating the performance of BISDN signalling systems under design. New performance parameters are introduced and their network-dependent values are extracted through a message flow model which has the capability to describe the impact of call and bearer control separation on the signalling performance. Signalling protocols are modelled through a modular decomposition of the seven OSI layers including the service user to three submodels. The workload model is user descriptive in the sense that it does not approximate the direct input traffic required for evaluating the performance of a layer protocol; instead, through a multi-level approach, it describes the actual implications of user signalling activity for the general signalling traffic. The signalling protocol model is derived from the global functional model of the signalling protocols and information flows using a network of queues incorporating synchronization and dependency functions. The same queueing approach is followed for the signalling transfer network which is used to define processing speed and signalling bandwidth requirements and to identify possible performance bottlenecks stemming from the realization of the related protocols

    Chemotaxis: a feedback-based computational model robustly predicts multiple aspects of real cell behaviour

    Get PDF
    The mechanism of eukaryotic chemotaxis remains unclear despite intensive study. The most frequently described mechanism acts through attractants causing actin polymerization, in turn leading to pseudopod formation and cell movement. We recently proposed an alternative mechanism, supported by several lines of data, in which pseudopods are made by a self-generated cycle. If chemoattractants are present, they modulate the cycle rather than directly causing actin polymerization. The aim of this work is to test the explanatory and predictive powers of such pseudopod-based models to predict the complex behaviour of cells in chemotaxis. We have now tested the effectiveness of this mechanism using a computational model of cell movement and chemotaxis based on pseudopod autocatalysis. The model reproduces a surprisingly wide range of existing data about cell movement and chemotaxis. It simulates cell polarization and persistence without stimuli and selection of accurate pseudopods when chemoattractant gradients are present. It predicts both bias of pseudopod position in low chemoattractant gradients and-unexpectedly-lateral pseudopod initiation in high gradients. To test the predictive ability of the model, we looked for untested and novel predictions. One prediction from the model is that the angle between successive pseudopods at the front of the cell will increase in proportion to the difference between the cell's direction and the direction of the gradient. We measured the angles between pseudopods in chemotaxing Dictyostelium cells under different conditions and found the results agreed with the model extremely well. Our model and data together suggest that in rapidly moving cells like Dictyostelium and neutrophils an intrinsic pseudopod cycle lies at the heart of cell motility. This implies that the mechanism behind chemotaxis relies on modification of intrinsic pseudopod behaviour, more than generation of new pseudopods or actin polymerization by chemoattractant

    Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments

    Get PDF
    One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment

    The use of hybrid cellular automaton models for improving cancer therapy, In Proceedings, Cellular Automata: 6th International Conference on Cellular Automata for Research and Industry, ACRI 2004, Amsterdam, The Netherlands, eds P.M.A. Sloot, B. Chopard, A.G. Hoekstra

    Get PDF
    The Hybrid Cellular Automata (HCA) modelling framework can be an efficient approach to a number of biological problems, particularly those which involve the integration of multiple spatial and temporal scales. As such, HCA may become a key modelling tool in the development of the so-called intergrative biology. In this paper, we first discuss HCA on a general level and then present results obtained when this approach was implemented in cancer research

    Clustering and Signalling of Cell Receptors

    Get PDF
    As a response to ligand binding, transmembrane cell receptors often enhance their clustering, or oligomerization, during the signalling process. Here we present a statistical mechanical model which combines the aspects of clustering and signalling. In this model, receptors float on the surface, while for two neighboring receptors, there is an interaction energy dependent on their conformational states. On the other hand, ligand binding of a receptor shifts the energy difference between the two conformational states. Due to thermal fluctuation, the effects of clustering and signalling are statistical average quantities. This model reduces to a floating Ising model with a random field. We calculate the signalling in a grand canonical ensemble mean field approach, using Hubbard-Stratonovich transformation and replica method. Monte Carlo simulations are also performed. Essential biological features are obtained in our model.Comment: revtex, 16 pages, including figures, final versio
    • 

    corecore