General broadcasting algorithms in one-port wormhole routed hypercubes

Wormhole routing has been accepted as an efficient switching mechanism in point-to-point interconnection networks. Here the network resource, i.e. node buffers and communication channels, are effectively utilized to deliver message across the network; We consider the problem of broadcasting a message in the hypercue equipped with the wormhole switching mechanism. The model is a generalization of an earlier work and considers a broadcast path-length of {dollar}m\ (1\leq m\leq n{dollar}) in the n-cube with a single-port communication capability. In this thesis, the scheme of e-cube and a Gray code path routing and intermediate reception capability have been adopted in order to solve the problem of broadcasting in one-port wormhole routed hypercubes. Two methods have been suggested; one is based on utilizing the Gray codes (Gray code path-based routing), while the other is based on the recursive partitioning of the cube (cube-based routing). The number of routing steps in both methods are compared to those in the previous results, as well as to the lower bounds derived based on the path-length m assumption. A cube-based and a path-based algorithm give {dollar}T(R)+(k\sb{c}+1)T(m){dollar} and {dollar}k\sb{G} +T(m){dollar} routing steps, respectively. By comparison with routing steps of both algorithms, the performance of the path-based algorithm shows better than that of the cube-based; The results of this work are significant and can be used for immediate implementation in contemporary machines most of which are equipped with wormhole routing and serial communication capability

Network and multi-scale signal analysis for the integration of large omic datasets: applications in \u3ci\u3ePopulus trichocarpa\u3c/i\u3e

Poplar species are promising sources of cellulosic biomass for biofuels because of their fast growth rate, high cellulose content and moderate lignin content. There is an increasing movement on integrating multiple layers of ’omics data in a systems biology approach to understand gene-phenotype relationships and assist in plant breeding programs. This dissertation involves the use of network and signal processing techniques for the combined analysis of these various data types, for the goals of (1) increasing fundamental knowledge of P. trichocarpa and (2) facilitating the generation of hypotheses about target genes and phenotypes of interest. A data integration “Lines of Evidence” method is presented for the identification and prioritization of target genes involved in functions of interest. A new post-GWAS method, Pleiotropy Decomposition, is presented, which extracts pleiotropic relationships between genes and phenotypes from GWAS results, allowing for identification of genes with signatures favorable to genome editing. Continuous wavelet transform signal processing analysis is applied in the characterization of genome distributions of various features (including variant density, gene density, and methylation profiles) in order to identify chromosome structures such as the centromere. This resulted in the approximate centromere locations on all P. trichocarpa chromosomes, which had previously not been adequately reported in the scientific literature. Discrete wavelet transform signal processing followed by correlation analysis was applied to genomic features from various data types including transposable element density, methylation density, SNP density, gene density, centromere position and putative ancestral centromere position. Subsequent correlation analysis of the resulting wavelet coefficients identified scale-specific relationships between these genomic features, and provide insights into the evolution of the genome structure of P. trichocarpa. These methods have provided strategies to both increase fundamental knowledge about the P. trichocarpa system, as well as to identify new target genes related to biofuels targets. We intend that these approaches will ultimately be used in the designing of better plants for more efficient and sustainable production of bioenergy

Integrated topological representation of multi-scale utility resource networks

PhD ThesisThe growth of urban areas and their resource consumption presents a significant global challenge. Existing utility resource supply systems are unresponsive, unreliable and costly. There is a need to improve the configuration and management of the infrastructure networks that carry these resources from source to consumer and this is best performed through analysis of multi-scale, integrated digital representations. However, the real-world networks are represented across different datasets that are underpinned by different data standards, practices and assumptions, and are thus challenging to integrate. Existing integration methods focus predominantly on achieving maximum information retention through complex schema mappings and the development of new data standards, and there is strong emphasis on reconciling differences in geometries. However, network topology is of greatest importance for the analysis of utility networks and simulation of utility resource flows because it is a representation of functional connectivity, and the derivation of this topology does not require the preservation of full information detail. The most pressing challenge is asserting the connectivity between the datasets that each represent subnetworks of the entire end-to-end network system. This project presents an approach to integration that makes use of abstracted digital representations of electricity and water networks to infer inter-dataset network connectivity, exploring what can be achieved by exploiting commonalities between existing datasets and data standards to overcome their otherwise inhibiting disparities. The developed methods rely on the use of graph representations, heuristics and spatial inference, and the results are assessed using surveying techniques and statistical analysis of uncertainties. An algorithm developed for water networks was able to correctly infer a building connection that was absent from source datasets. The thesis concludes that several of the key use cases for integrated topological representation of utility networks are partially satisfied through the methods presented, but that some differences in data standardisation and best practice in the GIS and BIM domains prevent full automation. The common and unique identification of real-world objects, agreement on a shared concept vocabulary for the built environment, more accurate positioning of distribution assets, consistent use of (and improved best practice for) georeferencing of BIM models and a standardised numerical expression of data uncertainties are identified as points of development.Engineering and Physical Sciences Research Council Ordnance Surve

Computational Studies on the Evolution of Metabolism

Living organisms throughout evolution have developed desired properties, such as the ability of maintaining functionality despite changes in the environment or their inner structure, the formation of functional modules, from metabolic pathways to organs, and most essentially the capacity to adapt and evolve in a process called natural selection. It can be observed in the metabolic networks of modern organisms that many key pathways such as the citric acid cycle, glycolysis, or the biosynthesis of most amino acids are common to all of them. Understanding the evolutionary mechanisms behind this development of complex biological systems is an intriguing and important task of current research in biology as well as artificial life. Several competing hypotheses for the formation of metabolic pathways and the mecha- nisms that shape metabolic networks have been discussed in the literature, each of which finds support from comparative analysis of extant genomes. However, while being powerful tools for the investigation of metabolic evolution, these traditional methods do not allow to look back in evolution far enough to the time when metabolism had to emerge and evolve to the form we can observe today. To this end, simulation studies have been introduced to discover the principles of metabolic evolution and the sources for the emergence of metabolism prop- erties. These approaches differ considerably in the realism and explicitness of the underlying models. A difficult trade-off between realism and computational feasibility has to be made and further modeling decisions on many scales have to be taken into account, requiring the combination of knowledge from different fields such as chemistry, physics, biology and last but not least also computer science. In this thesis, a novel computational model for the in silico evolution of early metabolism is introduced. It comprises all the components on different scales to resemble a situation of evolving metabolic protocells in an RNA-world. Therefore, the model contains a minimal RNA-based genetics and an evolving metabolism of catalytic ribozymes that manipulate a rich underlying chemistry. To allow the metabolic organization to escape from the confines of the chemical space set by the initial conditions of the simulation and in general an open- ended evolution, an evolvable sequence-to-function map is used. At the heart of the metabolic subsystem is a graph-based artificial chemistry equipped with a built-in thermodynamics. The generation of the metabolic reaction network is realized as a rule-based stochastic simulation. The necessary reaction rates are calculated from the chemical graphs of the reactants on the fly. The selection procedure among the population of protocells is based on the optimal metabolic yield of the protocells, which is computed using flux balance analysis. The introduced computational model allows for profound investigations of the evolution of early metabolism and the underlying evolutionary mechanisms. One application in this thesis is the study of the formation of metabolic pathways. Therefore, four established hypothe- ses, namely the backwards evolution, forward evolution, patchwork evolution and the shell hypothesis, are discussed within the realms of this in silico evolution study. The metabolic pathways of the networks, evolved in various simulation runs, are determined and analyzed in terms of their evolutionary direction. The simulation results suggest that the seemingly mutually exclusive hypotheses may well be compatible when considering that different pro- cesses dominate different phases in the evolution of a metabolic system. Further, it is found that forward evolution shapes the metabolic network in the very early steps of evolution. In later and more complex stages, enzyme recruitment supersedes forward evolution, keeping a core set of pathways from the early phase. Backward evolution can only be observed under conditions of steady environmental change. Additionally, evolutionary history of enzymes and metabolites were studied on the network level as well as for single instances, showing a great variety of evolutionary mechanisms at work. The second major focus of the in silico evolutionary study is the emergence of complex system properties, such as robustness and modularity. To this end several techniques to analyze the metabolic systems were used. The measures for complex properties stem from the fields of graph theory, steady state analysis and neutral network theory. Some are used in general network analysis and others were developed specifically for the purpose introduced in this work. To discover potential sources for the emergence of system properties, three different evolutionary scenarios were tested and compared. The first two scenarios are the same as for the first part of the investigation, one scenario of evolution under static conditions and one incorporating a steady change in the set of ”food” molecules. A third scenario was added that also simulates a static evolution but with an increased mutation rate and regular events of horizontal gene transfer between protocells of the population. The comparison of all three scenarios with real world metabolic networks shows a significant similarity in structure and properties. Among the three scenarios, the two static evolutions yield the most robust metabolic networks, however, the networks evolved under environmental change exhibit their own strategy to a robustness more suited to their conditions. As expected from theory, horizontal gene transfer and changes in the environment seem to produce higher degrees of modularity in metabolism. Both scenarios develop rather different kinds of modularity, while horizontal gene transfer provides for more isolated modules, the modules of the second scenario are far more interconnected

Computational Studies on the Evolution of Metabolism

Living organisms throughout evolution have developed desired properties, such as the ability of maintaining functionality despite changes in the environment or their inner structure, the formation of functional modules, from metabolic pathways to organs, and most essentially the capacity to adapt and evolve in a process called natural selection. It can be observed in the metabolic networks of modern organisms that many key pathways such as the citric acid cycle, glycolysis, or the biosynthesis of most amino acids are common to all of them. Understanding the evolutionary mechanisms behind this development of complex biological systems is an intriguing and important task of current research in biology as well as artificial life. Several competing hypotheses for the formation of metabolic pathways and the mecha- nisms that shape metabolic networks have been discussed in the literature, each of which finds support from comparative analysis of extant genomes. However, while being powerful tools for the investigation of metabolic evolution, these traditional methods do not allow to look back in evolution far enough to the time when metabolism had to emerge and evolve to the form we can observe today. To this end, simulation studies have been introduced to discover the principles of metabolic evolution and the sources for the emergence of metabolism prop- erties. These approaches differ considerably in the realism and explicitness of the underlying models. A difficult trade-off between realism and computational feasibility has to be made and further modeling decisions on many scales have to be taken into account, requiring the combination of knowledge from different fields such as chemistry, physics, biology and last but not least also computer science. In this thesis, a novel computational model for the in silico evolution of early metabolism is introduced. It comprises all the components on different scales to resemble a situation of evolving metabolic protocells in an RNA-world. Therefore, the model contains a minimal RNA-based genetics and an evolving metabolism of catalytic ribozymes that manipulate a rich underlying chemistry. To allow the metabolic organization to escape from the confines of the chemical space set by the initial conditions of the simulation and in general an open- ended evolution, an evolvable sequence-to-function map is used. At the heart of the metabolic subsystem is a graph-based artificial chemistry equipped with a built-in thermodynamics. The generation of the metabolic reaction network is realized as a rule-based stochastic simulation. The necessary reaction rates are calculated from the chemical graphs of the reactants on the fly. The selection procedure among the population of protocells is based on the optimal metabolic yield of the protocells, which is computed using flux balance analysis. The introduced computational model allows for profound investigations of the evolution of early metabolism and the underlying evolutionary mechanisms. One application in this thesis is the study of the formation of metabolic pathways. Therefore, four established hypothe- ses, namely the backwards evolution, forward evolution, patchwork evolution and the shell hypothesis, are discussed within the realms of this in silico evolution study. The metabolic pathways of the networks, evolved in various simulation runs, are determined and analyzed in terms of their evolutionary direction. The simulation results suggest that the seemingly mutually exclusive hypotheses may well be compatible when considering that different pro- cesses dominate different phases in the evolution of a metabolic system. Further, it is found that forward evolution shapes the metabolic network in the very early steps of evolution. In later and more complex stages, enzyme recruitment supersedes forward evolution, keeping a core set of pathways from the early phase. Backward evolution can only be observed under conditions of steady environmental change. Additionally, evolutionary history of enzymes and metabolites were studied on the network level as well as for single instances, showing a great variety of evolutionary mechanisms at work. The second major focus of the in silico evolutionary study is the emergence of complex system properties, such as robustness and modularity. To this end several techniques to analyze the metabolic systems were used. The measures for complex properties stem from the fields of graph theory, steady state analysis and neutral network theory. Some are used in general network analysis and others were developed specifically for the purpose introduced in this work. To discover potential sources for the emergence of system properties, three different evolutionary scenarios were tested and compared. The first two scenarios are the same as for the first part of the investigation, one scenario of evolution under static conditions and one incorporating a steady change in the set of ”food” molecules. A third scenario was added that also simulates a static evolution but with an increased mutation rate and regular events of horizontal gene transfer between protocells of the population. The comparison of all three scenarios with real world metabolic networks shows a significant similarity in structure and properties. Among the three scenarios, the two static evolutions yield the most robust metabolic networks, however, the networks evolved under environmental change exhibit their own strategy to a robustness more suited to their conditions. As expected from theory, horizontal gene transfer and changes in the environment seem to produce higher degrees of modularity in metabolism. Both scenarios develop rather different kinds of modularity, while horizontal gene transfer provides for more isolated modules, the modules of the second scenario are far more interconnected

NoC-based Architectures for Real-Time Applications : Performance Analysis and Design Space Exploration

Monoprocessor architectures have reached their limits in regard to the computing power they offer vs the needs of modern systems. Although multicore architectures partially mitigate this limitation and are commonly used nowadays, they usually rely on intrinsically non-scalable buses to interconnect the cores. The manycore paradigm was proposed to tackle the scalability issue of bus-based multicore processors. It can scale up to hundreds of processing elements (PEs) on a single chip, by organizing them into computing tiles (holding one or several PEs). Intercore communication is usually done using a Network-on-Chip (NoC) that consists of interconnected onchip routers allowing communication between tiles. However, manycore architectures raise numerous challenges, particularly for real-time applications. First, NoC-based communication tends to generate complex blocking patterns when congestion occurs, which complicates the analysis, since computing accurate worst-case delays becomes difficult. Second, running many applications on large Systems-on-Chip such as manycore architectures makes system design particularly crucial and complex. On one hand, it complicates Design Space Exploration, as it multiplies the implementation alternatives that will guarantee the desired functionalities. On the other hand, once a hardware architecture is chosen, mapping the tasks of all applications on the platform is a hard problem, and finding an optimal solution in a reasonable amount of time is not always possible. Therefore, our first contributions address the need for computing tight worst-case delay bounds in wormhole NoCs. We first propose a buffer-aware worst-case timing analysis (BATA) to derive upper bounds on the worst-case end-to-end delays of constant-bit rate data flows transmitted over a NoC on a manycore architecture. We then extend BATA to cover a wider range of traffic types, including bursty traffic flows, and heterogeneous architectures. The introduced method is called G-BATA for Graph-based BATA. In addition to covering a wider range of assumptions, G-BATA improves the computation time; thus increases the scalability of the method. In a second part, we develop a method addressing design and mapping for applications with real-time constraints on manycore platforms. It combines model-based engineering tools (TTool) and simulation with our analytical verification technique (G-BATA) and tools (WoPANets) to provide an efficient design space exploration framework. Finally, we validate our contributions on (a) a serie of experiments on a physical platform and (b) two case studies taken from the real world: an autonomous vehicle control application, and a 5G signal decoder applicatio

Mathematical models of cellular decisions: investigating immune response and apoptosis

The main objective of this thesis is to develop and analyze mathematical models of cellular decisions. This work focuses on understanding the mechanisms involved in specific cellular processes such as immune response in the vascular system, and those involved in apoptosis, or programmed cellular death. A series of simple ordinary differential equation (ODE) models are constructed describing the macrophage response to hemoglobin:haptoglobin (Hb:Hp) complexes that may be present in vascular inflammation. The models proposed a positive feedback loop between the CD163 macrophage receptor and anti-inflammatory cytokine interleukin-10 (IL-10) and bifurcation analysis predicted the existence of a cellular phenotypic switch which was experimentally verified. Moreover, these models are extended to include the intracellular mediator heme oxygenase-1 (HO-1). Analysis of the proposed models find a positive feedback mechanism between IL-10 and HO-1. This model also predicts cellular response of heme and IL-10 stimuli. For the apoptotic (cell suicide) system, a modularized model is constructed encompassing the extrinsic and intrinsic signaling pathways. Model reduction is performed by abstracting the dynamics of complexes (oligomers) at a steady-state. This simplified model is analyzed, revealing different kinetic properties between type I and type II cells, and reduced models verify results. The second model of apoptosis proposes a novel mechanism of apoptosis activation through receptor-ligand clustering, yielding robust bistability and hysteresis. Using techniques from algebraic geometry, a model selection criterion is provided between the proposed and existing model as experimental data becomes available to verify the mechanism. The models developed throughout this thesis reveal important and relevant mechanisms specific to cellular response; specifically, interactions necessary for an organism to maintain homeostasis are identified. This work enables a deeper understanding of the biological interactions and dynamics of vascular inflammation and apoptosis. The results of these models provide predictions which may motivate further experimental work and theoretical study

Performance Evaluation of Distributed Security Protocols Using Discrete Event Simulation

The Border Gateway Protocol (BGP) that manages inter-domain routing on the Internet lacks security. Protective measures using public key cryptography introduce complexities and costs. To support authentication and other security functionality in large networks, we need public key infrastructures (PKIs). Protocols that distribute and validate certificates introduce additional complexities and costs. The certification path building algorithm that helps users establish trust on certificates in the distributed network environment is particularly complicated. Neither routing security nor PKI come for free. Prior to this work, the research study on performance issues of these large-scale distributed security systems was minimal. In this thesis, we evaluate the performance of BGP security protocols and PKI systems. We answer the questions about how the performance affects protocol behaviors and how we can improve the efficiency of these distributed protocols to bring them one step closer to reality. The complexity of the Internet makes an analytical approach difficult; and the scale of Internet makes empirical approaches also unworkable. Consequently, we take the approach of simulation. We have built the simulation frameworks to model a number of BGP security protocols and the PKI system. We have identified performance problems of Secure BGP (S-BGP), a primary BGP security protocol, and proposed and evaluated Signature Amortization (S-A) and Aggregated Path Authentication (APA) schemes that significantly improve efficiency of S-BGP without compromising security. We have also built a simulation framework for general PKI systems and evaluated certification path building algorithms, a critical part of establishing trust in Internet-scale PKI, and used this framework to improve algorithm performance