Optimization of Spectrum Allocation in Cognitive Radio and Dynamic Spectrum Access Networks

Spectrum has become a treasured commodity. However, many licensed frequency bands exclusively assigned to the primary license holders (also called primary users) remain relatively unused or under-utilized for most of the time. Allowing other users (also called secondary users) without a license to operate in these bands with no interference becomes a promising way to satisfy the fast growing needs for frequency spectrum resources. A cognitive radio adapts to the environment it operates in by sensing the spectrum and quickly decides on appropriate frequency bands and transmission parameters to use in order to achieve certain performance goals. One of the most important issues in cognitive radio networks (CRNs) is intelligent channel allocation which will improve the performance of the network and spectrum utilization. The objective of this dissertation is to address the channel allocation optimization problem in cognitive radio and DSA networks under both centralized architecture and distributed architecture. By centralized architecture we mean the cognitive radio and DSA networks are infrastructure based. That is, there is a centralized device which collects all information from other cognitive radios and produces a channel allocation scheme. Then each secondary user follows the spectrum allocation and accesses the corresponding piece of spectrum. By distributed architecture we mean that each secondary user inside the cognitive radio and DSA networks makes its own decision based on local information on the spectrum usage. Each secondary user only considers the spectrum usage around itself. We studied three common objectives of the channel allocation optimization problem, including maximum network throughput (MNT), max-min fairness (MMF), and proportional fairness (PF). Given different optimization objectives, we developed mathematical models in terms of linear programing and non-linear programing formulations, under the centralized architecture. We also designed a unified framework with different heuristic algorithms for different optimization objectives and the best results from different algorithms can be automatically chosen without manual intervention. We also conducted additional work on spectrum allocation under distributed architecture. First, we studied the channel availability prediction problem. Since there is a lot of usable statistic information on spectrum usage from national and regional agencies, we presented a Bayesian inference based prediction method, which utilizes prior information to make better prediction on channel availability. Finally a distributed channel allocation algorithm is designed based on the channel prediction results. We illustrated that the interaction behavior between different secondary users can be modeled as a game, in which the secondary users are denoted as players and the channels are denoted as resources. We proved that our distributed spectrum allocation algorithm can achieve to Nash Equilibrium, and is Pareto optimal

Inverse Problems in data-driven multi-scale Systems Medicine: application to cancer physiology

Systems Medicine is an interdisciplinary framework involving reciprocal feedback between clinical investigation and mathematical modeling/analysis. Its aim is to improve the understanding of complex diseases by integrating knowledge and data across multiple levels of biological organization. This Thesis focuses on three inverse problems, arising from three kinds of data and related to cancer physiology, at different scales: tissues, cells, molecules. The general assumption of this piece of research is that cancer is associated toa path ological glucose consumption and, in fact, its functional behavior can be assessed by nuclear medicine experiments using [18F]-fluorodeoxyglucose (FDG) as a radioactive tracer mimicking the glucose properties. At tissue-scale, this Thesis considers the Positron Emission Tomography (PET) imaging technique, and deals with two distinct issues within compartmental analysis. First, this Thesis presents a compartmental approach, referred to as reference tissue model, for the estimation of FDG kinetics inside cancer tissues when the arterial blood input of the system is unknown. Then, this Thesis proposes an efficient and reliable method for recovering the compartmental kinetic parameters for each PET image pixel in the context of parametric imaging, exploiting information on the tissue physiology. Standard models in compartmental analysis assume that phosphorylation and dephosphorylation of FDG occur in the same intracellular cytosolic volume. Advances in cell biochemistry have shown that the appropriate location of dephosphorylation is the endoplasmic reticulum (ER). Therefore, at cell-scale, this Thesis formalizes a biochemically-driven compartmental model accounting for the specific role played by the ER, and applies it to the analysis of in vitro experiments on FDG uptake by cancer cell cultures obtained with a LigandTracer (LT) device. Finally, at molecule-scale, this Thesis provides a preliminary mathematical investigation of a chemical reaction network (CRN), represented by a huge Molecular Interaction Map (MIM), describing the biochemical interactions occurring between signaling proteins in specific pathways within a cancer cell. The main issue addressed in this case is the network parameterization problem, i.e. how to determine the reaction rate coefficients from protein concentration data

Analyses of CRN effectors (Crinkler and Necrosis) of the oomycete Aphanomyces euteiches

L'oomycète Aphanomyces euteiches est un pathogène racinaire de légumineuses cultivées (pois, luzerne ...) et de la plante modèle Medicago truncatula. Les oomycètes, comme d'autres microorganismes pathogènes eucaryotes, secrètent et transloquent des molécules à l'intérieur des cellules de l'hôte (effecteurs intracellulaires/cytoplasmiques) dans le but de manipuler les fonctions de la plante et de faciliter l'infection. Les protéines CRN (Crinkling and Necrosis) constituent une famille d'effecteurs nucléaires largement répandue chez les oomycètes et récemment décrites chez des espèces fongiques. Leurs cibles et rôle dans la virulence ainsi que leurs mécanismes de sécrétion et de translocation sont encore mal compris. Nous avons entrepris la caractérisation fonctionnelle des protéines AeCRN5 et AeCRN13 d'A.euteiches ainsi que de l'homologue d'AeCRN13 du champignon pathogène d'amphibien Batrachochytrium dendrobatidis, BdCRN13. Les analyses d'expression génique et protéique ont permis de montrer que AeCRN5 et AeCRN13 sont exprimés durant l'infection des racines de M. truncatula. Des résultats préliminaires d'immuno-localisation d'AeCRN13 ont révélé, pour la première fois, la sécrétion et translocation d'un CRN durant l'infection. Leur expression hétérologue, à la fois dans les cellules de plantes et d'amphibiens, a montré que ces protéines se localisent dans les noyaux où leurs activités conduisent à la perturbation de la physiologie de l'hôte. En développant un système in vivo basé sur la technique de FRET-FLIM, nous avons démontré que ces CRN ciblent les acides nucléiques: AeCRN5 cible l'ARN des plantes tandis qu'AeCRN13 et BdCRN13 lient l'ADN. Ces deux effecteurs CRN13 exhibent un motif de type HNH, lequel est typiquement retrouvé dans des endonucleases. Nous avons démontré que les CRN13 présentent une activité nuclease in vivo conduisant à la génération de coupures double brin de l'ADN. Ce travail a permis de mettre en évidence un nouvel mécanisme d'action des effecteurs de microorganismes eucaryotes et apporte des nouveaux aspects pour la compréhension de l'activité des protéines CRN d'oomycète mais aussi, pour la première fois, de champignon.The oomycete Aphanomyces euteiches is an important pathogen infecting roots of legumes (pea, alfalfa...) and the model legume Medicago truncatula. Oomycetes and other microbial eukaryotic pathogens secrete and deliver effector molecules into host intracellular compartments (intracellular/cytoplasmic effectors) to manipulate plant functions and promote infection. CRN (Crinkling and Necrosis) proteins are a wide class of intracellular, nuclear-localized effectors commonly found in oomycetes and recently described in true fungi whose host targets, virulence roles, secretion and host-delivery mechanisms are poorly understood. We addressed the functional characterization of CRN proteins AeCRN5 and AeCRN13 of A. euteiches and AeCRN13's homolog of the chytrid fungal pathogen of amphibians Batrachochytrium dendrobatidis, BdCRN13. Gene and protein expression studies showed that AeCRN5 and AeCRN13 are expressed during infection of M. truncatula's roots. Preliminary immunolocalization studies on AeCRN13 in infected roots indicated that the protein is secreted and translocated into root cells, depicting for the first time CRN secretion and translocation into the host during infection. The heterologous ectopic expression of AeCRNs and BdCRN13 in plant and amphibian cells indicated that these proteins target host nuclei and lead to the perturbation of host physiology. By developing an in vivo FRET-FLIM-based assay, we revealed that these CRNs target host nucleic acids: AeCRN5 targets plant RNA while AeCRN13 and BdCRN13 target DNA. Both CRN13 exhibit a HNH-like motif commonly found in endonucleases and we further demonstrated that both CRN13 display a nuclease activity in vivo inducing double-stranded DNA cleavage. This work reveals a new mode of action of intracellular eukaryotic effectors and brings new aspects for the comprehension of CRN's activities not only in oomycetes but, for the first time, also in true fungi

Unweaving complex reactivity: graph-based tools to handle chemical reaction networks

La informació a nivell molecular obtinguda mitjançant estudis "in silico" s’ha establert com una eina essencial per a la caracterització de mecanismes de reacció complexos. A més, l’aplicabilitat de la química computacional s’ha vist substancialment ampliada a causa de l’increment continuat de la potència de càlcul disponible durant les darreres dècades. Així, no només han augmentat la precisió dels mètodes a utilitzar o la mida dels sistemes a modelitzar sinó també el grau de detall que es pot aconseguir en les descripcions mecanístiques resultants. Tanmateix, aquestes caracteritzacions més profundes, usualment assistides per tècniques d’automatització que permeten l’exploració de regions més extenses de l’espai químic, suposen un increment de la complexitat dels sistemes estudiats i per tant una limitació de la seva interpretabilitat. En aquesta Tesi s’han proposat, desenvolupat i posat a prova diverses eines amb el fi de fer el processament d’aquest tipus de xarxes de reacció químiques (CRNs) més simple i millorar la comprensió de processos reactius i catalítics complexos. Aquesta col·lecció d’eines té com fonament la utilització de grafs per modelitzar les xarxes (CRNs) corresponents, per poder fer servir els mètodes de la Teoria de Grafs (cerca de camins, isomorfismes...) en un context químic. Més concretament, aquestes eines inclouen amk-tools, una llibreria per a la visualització interactiva de xarxes de reacció descobertes de manera automàtica, gTOFfee, per a l’aplicació del "energy span model" pel càlcul de la freqüència de recanvi de cicles catalítics complexos calculats computacionalment, i OntoRXN, una ontologia per descriure CRNs de forma semàntica, integrant la topologia de la xarxa i la informació calculada en una única entitat organitzada segons els principis del "Semantic Data".La información a nivel molecular obtenida por medio de estudios "in silico" se ha convertido en una herramienta indispensable para la caracterización y comprensión de mecanismos de reacción complejos. Asimismo, la aplicabilidad de la química computacional se ha ampliado sustancialmente como consecuencia del continuo incremento de la potencia de cálculo durante las últimas décadas. Así, no sólo han aumentado la precisión de los métodos o el tamaño de los sistemas modelizables, sino también el grado de detalle en la descripción mecanística. Sin embargo, aumentar la profundidad de la caracterización de un sistema químico, usualmente a través de técnicas de automatización que permiten explorar ecciones más extensas del espacio químico, supone un aumento en la complejidad de los sistemas resultantes, dificultando la interpretación de los resultados. En esta Tesis se han propuesto, desarrollado y puesto a prueba distintas herramientas para simplificar el procesado de este tipo de redes de reacción químicas (CRNs), con el fin de mejorar la comprensión de procesos reactivos y catalíticos complejos. Este conjunto de herramientas se basa en el uso de grafos para modelizar las redes (CRNs) correspondientes, con tal de poder emplear los métodos de la Teoría de Grafos (búsqueda de caminos, isomorfismos...) bajo un contexto químico. Concretamente, estas herramientas incluyen amk-tools, para la visualización interactiva de redes de reacción descubiertas automáticamente, gTOFfee, para la aplicación del “energy span model” para calcular la frecuencia de recambio de ciclos catalíticos complejos caracterizados computacionalmente, y OntoRXN, una ontología para describir CRNs de manera semántica, integrando la topología de la red y la información calculada en una única entidad organizada bajo los principios del “Semantic Data”.The molecular-level insights gathered through "in silico" studies have become an essential asset for the elucidation and understanding of complex reaction mechanisms. Indeed, the applicability of computational chemistry has strongly widened due to the vast increase in computational power along the last decades. In this sense, not only the accuracy of the applied methods or the size of the target systems have increased, but also the level of detail attained for the mechanistic description. However, performing deeper descriptions of chemical systems, most often resorting to automation techniques that allow to easily explore larger parts of the chemical space, comes at the cost of also augmenting their complexity, rendering the results much harder to interpret. Throughout this Thesis, we have proposed, developed and tested a collection of tools aiming to process this kind of complex chemical reaction networks (CRNs), in order to provide new insights on reactive and catalytic processes. All of these tools employ graphs to model the target CRNs, in order to be able to use the methods of Graph Theory (e.g. path searches, isomorphisms...) in a chemical context. The tools that are discussed include amk-tools, a framework for the interactive visualization of automatically discovered reaction networks, gTOFfee, for the application of the energy span model to compute the turnover frequency of computationally characterized catalytic cycles, and OntoRXN, an ontology for the description of CRNs in a semantic manner integrating network topology and calculation information in a single, highly-structured entity

Stochastic spatial modelling of DNA methylation patterns and moment-based parameter estimation

In the first part of this thesis, we introduce and analyze spatial stochastic models for DNA methylation, an epigenetic mark with an important role in development. The underlying mechanisms controlling methylation are only partly understood. Several mechanistic models of enzyme activities responsible for methylation have been proposed. Here, we extend existing hidden Markov models (HMMs) for DNA methylation by describing the occurrence of spatial methylation patterns with stochastic automata networks. We perform numerical analysis of the HMMs applied to (non-)hairpin bisulfite sequencing KO data and accurately predict the wild-type data from these results. We find evidence that the activities of Dnmt3a/b responsible for de novo methylation depend on the left but not on the right CpG neighbors. The second part focuses on parameter estimation in chemical reaction networks (CRNs). We propose a generalized method of moments (GMM) approach for inferring the parameters of CRNs based on a sophisticated matching of the statistical moments of the stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when many samples are available. The GMM provides accurate and fast estimations of unknown parameters of CRNs. The accuracy increases and the variance decreases when higher-order moments are considered.Im ersten Teil der Arbeit führen wir eine Analyse für spatielle stochastische Modelle der DNA Methylierung, ein wichtiger epigenetischer Marker in der Entwicklung, durch. Die zugrunde liegenden Mechanismen der Methylierung werden noch nicht vollständig verstanden. Mechanistische Modelle beschreiben die Aktivität der Methylierungsenzyme. Wir erweitern bestehende Hidden Markov Models (HMMs) zur DNA Methylierung durch eine Stochastic Automata Networks Beschreibung von spatiellen Methylierungsmustern. Wir führen eine numerische Analyse der HMMs auf bisulfit-sequenzierten KO Datens¨atzen aus und nutzen die Resultate, um die Wildtyp-Daten erfolgreich vorherzusagen. Unsere Ergebnisse deuten an, dass die Aktivitäten von Dnmt3a/b, die überwiegend für die de novo Methylierung verantwortlich sind, nur vom Methylierungsstatus des linken, nicht aber vom rechten CpG Nachbarn abhängen. Der zweite Teil befasst sich mit Parameterschätzung in chemischen Reaktionsnetzwerken (CRNs). Wir führen eine Verallgemeinerte Momentenmethode (GMM) ein, die die statistischen Momente des stochastischen Modells an die Momente von Stichproben geschickt anpasst. Die GMM nutzt hier kürzlich entwickelte, momentenbasierte Näherungen, liefert Schätzer mit wünschenswerten statistischen Eigenschaften, wenn genügend Stichproben verfügbar sind, mit schnellen und genauen Schätzungen der unbekannten Parameter in CRNs. Momente höherer Ordnung steigern die Genauigkeit des Schätzers, während die Varianz sinkt

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding

Conservation Laws in Nonequilibrium Thermodynamics: Stochastic Processes, Chemical Reaction Networks, and Information Processing

Thermodynamics has a long history. It was established during the 19th century as a phenomenological theory grasping the principles underlying heat engines. In the 20th and 21st centuries its range of applicability was extended to nonequilibrium stochastic and chemical processes. However a systematic procedure to identify the thermodynamic forces at work in these systems was lacking. In this thesis, we provide one by making use of conservation laws. Of particular importance are the conservation laws which are broken when putting the system in contact with different reservoirs (thermostats or chemostats). These laws depend on the internal structure of the system and are specific to each system. We introduce a systematic procedure to identify them and show how they shape the entropy production (i.e. the dissipation) into fundamental contributions. Each of these provides precious insight on how to drive and control the system out of equilibrium. We first present our results at the level of phenomenological thermodynamics. We then show that they can be systematically derived for various dynamics: Markov jump processes used in stochastic thermodynamics, also including the chemical master equation, and deterministic chemical rate equations with and without diffusion, which are used to describe chemical reaction networks. Generalized nonequilibrium Landauer principles ensue form our theory. They predict that the minimal thermodynamic cost necessary to transform the system from an arbitrary nonequilibrium state to another can be expressed in terms of information metrics such as relative entropies between the equilibrium and nonequilibrium states of the system

Neural Dynamics of Learning and Performance of Fixed Sequences: Latency Pattern Reorganizations and the N-STREAMS Model

Fixed sequences performed from memory play a key role in human cultural behavior, especially in music and in rapid communication through speaking, handwriting, and typing. Upon first performance, fixed sequences are often produced slowly, but extensive practice leads to performance that is both fluid and as rapid as allowed by constraints inherent in the task or the performer. The experimental study of fixed sequence learning and production has generated a large database with some challenging findings, including practice-related reorganizations of temporal properties of performance. In this paper, we analyze this literature and identify a coherent set of robust experimental effects. Among these are both the sequence length effect on latency, a dependence of reaction time on sequence length, and practice-dependent lost of the lengths effect on latency. We then introduce a neural network architecture capable of explaining these effects. Called the NSTREAMS model, this multi-module architecture embodies the hypothesis that the brain uses several substrates for serial order representation and learning. The theory describes three such substrates and how learning autonomously modifies their interaction over the course of practice. A key feature of the architecture is the co-operation of a 'competitive queuing' performance mechanism with both fundamentally parallel ('priority-tagged') and fundamentally sequential ('chain-like') representations of serial order. A neurobiological interpretation of the architecture suggests how different parts of the brain divide the labor for serial learning and performance. Rhodes (1999) presents a complete mathematical model as implementation of the architecture, and reports successful simulations of the major experimental effects. It also highlights how the network mechanisms incorporated in the architecture compare and contrast with earlier substrates proposed for competitive queuing, priority tagging and response chaining.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N00014-95-1-0409); National Institute of Health (RO1 DC02852

Systems biology approaches to the dynamics of gene expression and chemical reactions

Systems biology is an emergent interdisciplinary field of study whose main goal is to understand the global properties and functions of a biological system by investigating its structure and dynamics [74]. This high-level knowledge can be reached only with a coordinated approach involving researchers with different backgrounds in molecular biology, the various omics (like genomics, proteomics, metabolomics), computer science and dynamical systems theory. The history of systems biology as a distinct discipline began in the 1960s, and saw an impressive growth since year 2000, originated by the increased accumulation of biological information, the development of high-throughput experimental techniques, the use of powerful computer systems for calculations and database hosting, and the spread of Internet as the standard medium for information diffusion [77]. In the last few years, our research group tried to tackle a set of systems biology problems which look quite diverse, but share some topics like biological networks and system dynamics, which are of our interest and clearly fundamental for this field. In fact, the first issue we studied (covered in Part I) was the reverse engineering of large-scale gene regulatory networks. Inferring a gene network is the process of identifying interactions among genes from experimental data (tipically microarray expression profiles) using computational methods [6]. Our aim was to compare some of the most popular association network algorithms (the only ones applicable at a genome-wide level) in different conditions. In particular we verified the predictive power of similarity measures both of direct type (like correlations and mutual information) and of conditional type (partial correlations and conditional mutual information) applied on different kinds of experiments (like data taken at equilibrium or time courses) and on both synthetic and real microarray data (for E. coli and S. cerevisiae). In our simulations we saw that all network inference algorithms obtain better performances from data produced with \u201cstructural\u201d perturbations (like gene knockouts at steady state) than with just dynamical perturbations (like time course measurements or changes of the initial expression levels). Moreover, our analysis showed differences in the performances of the algorithms: direct methods are more robust in detecting stable relationships (like belonging to the same protein complex), while conditional methods are better at causal interactions (e.g. transcription factor\u2013binding site interactions), especially in presence of combinatorial transcriptional regulation. Even if time course microarray experiments are not particularly useful for inferring gene networks, they can instead give a great amount of information about the dynamical evolution of a biological process, provided that the measurements have a good time resolution. Recently, such a dataset has been published [119] for the yeast metabolic cycle, a well-known process where yeast cells synchronize with respect to oxidative and reductive functions. In that paper, the long-period respiratory oscillations were shown to be reflected in genome-wide periodic patterns in gene expression. As explained in Part II, we analyzed these time series in order to elucidate the dynamical role of post-transcriptional regulation (in particular mRNA stability) in the coordination of the cycle. We found that for periodic genes, arranged in classes according either to expression profile or to function, the pulses of mRNA abundance have phase and width which are directly proportional to the corresponding turnover rates. Moreover, the cascade of events which occurs during the yeast metabolic cycle (and their correlation with mRNA turnover) reflects to a large extent the gene expression program observable in other dynamical contexts such as the response to stresses or stimuli. The concepts of network and of systems dynamics return also as major arguments of Part III. In fact, there we present a study of some dynamical properties of the so-called chemical reaction networks, which are sets of chemical species among which a certain number of reactions can occur. These networks can be modeled as systems of ordinary differential equations for the species concentrations, and the dynamical evolution of these systems has been theoretically studied since the 1970s [47, 65]. Over time, several independent conditions have been proved concerning the capacity of a reaction network, regardless of the (often poorly known) reaction parameters, to exhibit multiple equilibria. This is a particularly interesting characteristic for biological systems, since it is required for the switch-like behavior observed during processes like intracellular signaling and cell differentiation. Inspired by those works, we developed a new open source software package for MATLAB, called ERNEST, which, by checking these various criteria on the structure of a chemical reaction network, can exclude the multistationarity of the corresponding reaction system. The results of this analysis can be used, for example, for model discrimination: if for a multistable biological process there are multiple candidate reaction models, it is possible to eliminate some of them by proving that they are always monostationary. Finally, we considered the related property of monotonicity for a reaction network. Monotone dynamical systems have the tendency to converge to an equilibrium and do not present chaotic behaviors. Most biological systems have the same features, and are therefore considered to be monotone or near-monotone [85, 116]. Using the notion of fundamental cycles from graph theory, we proved some theoretical results in order to determine how distant is a given biological network from being monotone. In particular, we showed that the distance to monotonicity of a network is equal to the minimal number of negative fundamental cycles of the corresponding J-graph, a signed multigraph which can be univocally associated to a dynamical system

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)