Dynamic Biological Networks – The Stomatogastric Nervous System
Characterizing, describing, and extracting information from a network is by now one of the main goals of science, since the study of network currently draws the attention of several fields of research, as biology, economics, social science, computer science and so on. The main goal is to analyze networks in order to extract their emergent properties and to understand functionality of such complex systems. This work concerns the analysis of biological networks and the two main approaches are treated: the first based on the study of their topological structure, the second based on the dynamic properties of the system described by a network. Original methods are presented to extract information from a network through both static and dynamic approaches opening new perspectives in biological network analysis.
Biological networks encapsulate invaluable information about the roles of different entities and their interactions with each other. Analyzing these networks is essential to reveal evolutionary differences between different cells and organisms. An important type of analysis is the comparative analysis which aims at identifying functionally similar components of these networks. Analogous to sequence alignment which identifies sequence similarity, network alignment reveals similar connectivity patterns such as alternative paths and subnetworks. Additional to the comparative analysis, examining solely the topological structure led to discovery of the steady states and the modular organization that these networks exhibit. This book introduces (i) Alignment algorithms for metabolic networks that account for heterogeneous network elements, connected subnetwork mappings and the scalability problem; (ii) An algorithm that predicts functional similarity between reactions based on metabolic flux analysis; (iii) Efficient methods that identify steady states of Boolean regulatory networks; (iv) An algorithm that identifies dynamic modular structure of regulatory networks.
Finkelstein ?mathematical? Modeling Of Dynamic Biological Systems
Market failure create uncertainties and asset specificities to many entrepreneurs while at the same time providing preferential bargaining opportunities to the few, thus, posing huge transaction costs to the disadvantaged, the poor, and vulnerable sections of the society. In developing economies, where there exist poor entrepreneurs, alliances are not in formal patterns and most often not well developed, mainly, in small businesses to overcome an increasing transaction costs. Instead in such businesses, entrepreneurs develop evolutionary informal networks with external actors in keeping their business landscape sustainable. These networks comprising: social, industrial, and support, has not been well studied. This work examined small enterprises networking in Addis Ababa: the capital of Ethiopia. The findings, overall, revealed that entrepreneurs build strong informal networks and these networks have significant influences on small business growth, especially through developing contacts with other entrepreneurs implying the need to promote the sector through valuing and scaling-up of inter-firm networks as important tools for business success.
Artificial neural networks are made up of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks.
IT IS generally believed by the research community that the introduction of complex network functions—such as routing—in the optical domain will allow a better network utilisation, lower cost and footprint, and a more efficiency in energy usage. The new optical components and sub-systems intended for dynamic optical networking introduce new kinds of physical layer impairments in the optical signal. Consequently, the aim of this book was to first identify and characterise the physical layer impairments of dynamic optical networks, and then digital signal processing techniques were developed to mitigate them. The initial focus of this work was the design and characterisation of digital optical receivers for dynamic core optical networks. Digital receiver techniques allow for complex algorithms to be implemented in the digital domain, which usually outperform their analogue counterparts in performance and flexibility. Digital receiver technologies can be equally applied to optical access networks, which share many traits with dynamic core networks. A dual-rate digital receiver, capable of detecting optical packets at 10 and 1.25 Gb/s, was developed and characterised.
The increasing interest in dynamic circuit networks to support the high-speed and predictable-service requirements of applications leads to the creation of various experimental test beds. One area of networking research in these testbed projects is bandwidth sharing mechanisms. One reference point for dynamic bandwidth-sharing mechanisms in connection-oriented networks is the telephone network. The bandwidth-sharing mechanism used in the telephone network is referred to as the immediate-request mode because there is no provision for making advance reservations for circuits. While this mechanism can be used in new high-speed optical connection-oriented networks, some applications require high per-call bandwidth, and this bandwidth level is a large fraction of even the highest-rate links in use today, the combination of which makes the immediate-request mode unsuitable. Therefore, alternative bandwidth-sharing modes are needed for these networks and applications. This book discusses two new bandwidth-sharing mechanisms based on book-ahead reservation schemes, and presents both the analytical and simulation models for the two mechanisms.
Nowadays, data needed to be stored, scaled and analyzed is increasing rapidly in more and more research areas like biology, sociology, telecommunication, etc. Because of the limited information about them, their understanding was made difficult and, as a result, advancing in many fields faced with such complex systems was hindered. Using data analysis systems like networks makes all these issues have a smooth solution. The results obtained in trying to perceive such complex metters by using this technique is admirable. More and more research areas like the ones named before have known beautiful progress in understanding the complex way they operate.
This research proposes and analyses the Dynamic Counter-Based broadcasting scheme under a range of network operating conditions and applications; and demonstrates a clear benefit of the scheme when compared to its predecessors under a wide range of considered conditions. The first part of the book, sets a baseline study of the counter-based scheme analyzing it under various network operating conditions. The second part, attempts to establish the claim that alleviating existing stochastic counter-based scheme by dynamically setting threshold values according to local neighborhood density improves overall network efficiency. The third part, evaluates dynamic counting and tests its performance in some approximately realistic scenarios. The examples chosen are from the rapidly developing field of Vehicular Ad hoc Networks (VANETs). The last part, shows that routing overhead can be significantly reduced by applying dynamic counting in the route discovery process of a well-known routing protocol. The study shows a clear benefit of the proposed scheme in terms of average collision rate, saved rebroadcasts and end-to-end delay, while maintaining reachability.
The proliferation of multimedia data services has spurred the remarkable growth of the mobile cellular network industry. To optimize the resources and performance of mobile cellular networks, spectrum management and traffic congestion control are key issues. In large cities, traffic demands are heavier in the city center compared to the suburbs, with a large busiest to quietest traffic ratio. Dynamic cell sizing is a flexible traffic congestion control mechanism which increases the capacity of a network by adaptively varying the pilot power to modify the coverage areas of cells for optimum performance under various traffic conditions. In order to mitigate the occurrence of coverage holes in a dynamic cell sizing environment, it is important to prioritize cell attenuation based on the traffic load of adjacent cells. This book presents a novel framework for predictive priority-based cell attenuation in dynamic cell sizing.
Development of advanced techniques for biological network visualization is crucial for successful progress in the areas of systems-level biology and data-intensive bioinformatics. However, current techniques for biological network visualization fall short of expectations for representing extensive biological networks. In order to provide useful network visualization tools, new approaches have to be proposed and applied alongside with those most powerful features of current visualization systems. The resulting representation techniques have to be tested by applying to large-scale examples that would include metabolic, signaling and gene expression events.
In the next years, several offshore wind farms will be installed all over Europe. Due to the technologic advantages that voltage-source converters have when compared to other available options, there is a strong possibility that this will be the technology chosen to connect offshore wind farms. Such assumption intensifies the need of studying and simulating the control methods for these systems. Therefore, the purpose of this work is to present and explain all the models that are needed to simulate, with the necessary detail, the electric systems of the offshore wind farms, the high-voltage converters and the DC grid. The control method of the DC system is given and several methods to control the DC grid voltage are presented and compared. This way, it will be possible for several countries connected to the multi-terminal DC grid not only to receive energy from the offshore wind farms, according to desired criteria, but also to trade energy with other nations.
Graphs and networks are ubiquitous representations of computer, biological and social systems. In this book we study characteristic properties of networks that can help us to understand the systems they represent. Specifically, we study patterns of correlations between connected nodes in a network and provide mechanistic explanations for the origins of such correlations. We will show that computer, biological and social networks have some commonalities among them. On the other hand, they also have distinctive features, which are reflected in the patterns of correlations between connected nodes in the network. Finally, we will show these correlations can affect the behavior of different processes taking place on networks, such as the spreading of computer viruses, infectious diseases, or the association between genes and biological function.