Exploring Complex Networks with Graph Investigator Research Application

This paper describes Graph Investigator, the application intended for analysis of complex networks. A rich set of application functions is briefly described including graph feature generation, comparison, visualization and edition. The program enables to analyze global and local structural properties of networks with the use of various descriptors derived from graph theory. Furthermore, it allows to quantify inter-graph similarity by embedding graph patterns into low-dimensional space or distance measurement based on feature vectors. The set of available graph descriptors includes over eighty statistical and algebraic measures. We present two examples of real-world networks analysis performed with Graph Investigator: comparison of brain vasculature with structurally similar artificial networks and analysis of vertices importance in a macaque cortical connectivity network. The third example describes tracking parameters of artificial vascular network evolving in the process of angiogenesis, modelled with the use of cellular automata

Multiscale Agent-based Model of Tumor Angiogenesis

AbstractComputational models of cancer complement the biological study of tumor growth. However, existing modeling approaches can be both inefficient and inaccurate due to the difficulties of representing the complex interactions between cells and tissues. We present a three-dimensional multiscale agent-based model of tumor growth with angiogenesis. The model is designed to easily adapt to various cancer types, although we focus on breast cancer. It includes cellular (genetic control), tissue (cells, blood vessels, angiogenesis), and molecular (VEGF, diffusion) levels of representation. Unlike in most cancer models, both normally functioning tissue cells and tumor cells are included in the model. Tumors grow following the expected spheroid cluster pattern, with growth limited by available oxygen. Angiogenesis, the process by which tumors may encourage new vessel growth for nutrient diffusion, is modeled with a new discrete approach that we propose will decrease computational cost. Our results show that despite proposing these new abstractions, we see similar results to previously accepted angiogenesis models. This may indicate that a more discrete approach should be considered by modelers in the future

Lattice-Boltzmann simulations of cerebral blood flow

Computational haemodynamics play a central role in the understanding of blood behaviour in the cerebral vasculature, increasing our knowledge in the onset of vascular diseases and their progression, improving diagnosis and ultimately providing better patient prognosis. Computer simulations hold the potential of accurately characterising motion of blood and its interaction with the vessel wall, providing the capability to assess surgical treatments with no danger to the patient. These aspects considerably contribute to better understand of blood circulation processes as well as to augment pre-treatment planning. Existing software environments for treatment planning consist of several stages, each requiring significant user interaction and processing time, significantly limiting their use in clinical scenarios. The aim of this PhD is to provide clinicians and researchers with a tool to aid in the understanding of human cerebral haemodynamics. This tool employs a high performance fluid solver based on the lattice-Boltzmann method (coined HemeLB), high performance distributed computing and grid computing, and various advanced software applications useful to efficiently set up and run patient-specific simulations. A graphical tool is used to segment the vasculature from patient-specific CT or MR data and configure boundary conditions with ease, creating models of the vasculature in real time. Blood flow visualisation is done in real time using in situ rendering techniques implemented within the parallel fluid solver and aided by steering capabilities; these programming strategies allows the clinician to interactively display the simulation results on a local workstation. A separate software application is used to numerically compare simulation results carried out at different spatial resolutions, providing a strategy to approach numerical validation. This developed software and supporting computational infrastructure was used to study various patient-specific intracranial aneurysms with the collaborating interventionalists at the National Hospital for Neurology and Neuroscience (London), using three-dimensional rotational angiography data to define the patient-specific vasculature. Blood flow motion was depicted in detail by the visualisation capabilities, clearly showing vortex fluid ow features and stress distribution at the inner surface of the aneurysms and their surrounding vasculature. These investigations permitted the clinicians to rapidly assess the risk associated with the growth and rupture of each aneurysm. The ultimate goal of this work is to aid clinical practice with an efficient easy-to-use toolkit for real-time decision support

Neo-Aristotelian Perspectives on Contemporary Science

The last two decades have seen two significant trends emerging within the philosophy of science: the rapid development and focus on the philosophy of the specialised sciences, and a resurgence of Aristotelian metaphysics, much of which is concerned with the possibility of emergence, as well as the ontological status and indispensability of dispositions and powers in science. Despite these recent trends, few Aristotelian metaphysicians have engaged directly with the philosophy of the specialised sciences. Additionally, the relationship between fundamental Aristotelian concepts—such as "hylomorphism", "substance", and "faculties"—and contemporary science has yet to receive a critical and systematic treatment. Neo-Aristotelian Perspectives on Contemporary Science aims to fill this gap in the literature by bringing together essays on the relationship between Aristotelianism and science that cut across interdisciplinary boundaries. The chapters in this volume are divided into two main sections covering the philosophy of physics and the philosophy of the life sciences. Featuring original contributions from distinguished and early-career scholars, this book will be of interest to specialists in analytical metaphysics and the philosophy of science

Use of Inferential Statistics to Design Effective Communication Protocols for Wireless Sensor Networks

This thesis explores the issues and techniques associated with employing the principles of inferential statistics to design effective Medium Access Control (MAC), routing and duty cycle management strategies for multihop Wireless Sensor Networks (WSNs). The main objective of these protocols are to maximise the throughput of the network, to prolong the lifetime of nodes and to reduce the end-to-end delay of packets over a general network scenario without particular considerations for specific topology configurations, traffic patterns or routing policies. WSNs represent one of the leading-edge technologies that have received substantial research efforts due to their prominent roles in many applications. However, to design effective communication protocols for WSNs is particularly challenging due to the scarce resources of these networks and the requirement for large-scale deployment. The MAC, routing and duty cycle management protocols are amongst the important strategies that are required to ensure correct operations of WSNs. This thesis makes use of the inferential statistics field to design these protocols; inferential statistics was selected as it provides a rich design space with powerful approaches and methods. The MAC protocol proposed in this thesis exploits the statistical characteristics of the Gamma distribution to enable each node to adjust its contention parameters dynamically based on its inference for the channel occupancy. This technique reduces the service time of packets and leverages the throughput by improving the channel utilisation. Reducing the service time minimises the energy consumed in contention to access the channel which in turn prolongs the lifetime of nodes. The proposed duty cycle management scheme uses non-parametric Bayesian inference to enable each node to determine the best times and durations for its sleeping durations without posing overheads on the network. Hence the lifetime of node is prolonged by mitigating the amount of energy wasted in overhearing and idle listening. Prolonging the lifetime of nodes increases the throughput of the network and reduces the end-to-end delay as it allows nodes to route their packets over optimal paths for longer periods. The proposed routing protocol uses one of the state-of-the-art inference techniques dubbed spatial reasoning that enables each node to figure out the spatial relationships between nodes without overwhelming the network with control packets. As a result, the end-to-end delay is reduced while the throughput and lifetime are increased. Besides the proposed protocols, this thesis utilises the analytical aspects of statistics to develop rigorous analytical models that can accurately predict the queuing and medium access delay and energy consumption over multihop networks. Moreover, this thesis provides a broader perspective for design of communication protocols for WSNs by casting the operations of these networks in the domains of the artificial chemistry discipline and the harmony search optimisation algorithm

The validity and validation of mathematical models: Methodological, theoretical, and practical studies with emphasis on the modelling of complex biological systems

In recent years there has been a tremendous growth in the development and application of mathematical models in all areas of science and engineering. Aided by the advances and availability of computers, models have been used in many new areas, such as biology and the social sciences, and applied to increasingly complex systems. At the same time, model validity and validation have become correspondingly more problematic yet received little attention. The aims of this thesis are to clarify the meaning of model validity, to develop a range of procedures for model validation, and to consider in depth the validity of a number of specific models. The main focus is the use of models in systems science and in biology and medicine. A review of the scientific literature of model validity and validation is made which reveals many techniques for empirical validation, but exposes the lack of a consistent conceptual approach towards model validity. In reviewing the philosophy of science with reference to validity and validation, the importance of regarding models and validation as part of an evolving research programme and of heuristic considerations in assessing model validity are emphasised. A new and innovative theory of model validity is proposed which explicates model validity as a multidimensional concept closely related to modelling objectives. The different modelling objectives and types of data are classified and the various concepts of validity are expressed as validity criteria. The general relationship between modelling objectives, data, and validity criteria is explained. The theory is then used to devise a range of validation methodologies suitable for models in research areas at different stages of development. Models of the human cardiovascular, renal, and respiratory systems are used as case studies for validation. Extensive use is made of the conceptual framework of the theory of model validity and the validation methodologies. The results are a precise delimiting of the validity of the models, the areas of uncertainty, and the potential for future development. This indicates the critical value of the theory and the appropriateness of the methodologies to complex biological models. Further support for the theory and its wide applicability is obtained in using it to consider aspects of validity and validation of models in the social sciences. Finally, the implications of the work for modelling and validation in systems science and in biology and medicine are examined. In both areas it is shown that the theory of model validity leads to an improved understanding of the nature of modelling and validity, and that the validation methodologies are suitable for the critical and effective validation of a wide range of models. In biology and medicine specific recommendations are made for the types of model appropriate to different modelling objectives and for suitable techniques and methodologies for validation. This thesis contributes to an improved understanding of the concept of model validity and offers a repertoire of validation methodologies. On another level, it is a broad methodological study of the kind urgently required in systems science. More practically, however, much of the thesis is concerned with the detailed validation of three specific biological models