Network, degeneracy and bow tie. Integrating paradigms and architectures to grasp the complexity of the immune system

Recently, the network paradigm, an application of graph theory to biology, has proven to be a powerful approach to gaining insights into biological complexity, and has catalyzed the advancement of systems biology. In this perspective and focusing on the immune system, we propose here a more comprehensive view to go beyond the concept of network. We start from the concept of degeneracy, one of the most prominent characteristic of biological complexity, defined as the ability of structurally different elements to perform the same function, and we show that degeneracy is highly intertwined with another recently-proposed organizational principle, i.e. 'bow tie architecture'. The simultaneous consideration of concepts such as degeneracy, bow tie architecture and network results in a powerful new interpretative tool that takes into account the constructive role of noise (stochastic fluctuations) and is able to grasp the major characteristics of biological complexity, i.e. the capacity to turn an apparently chaotic and highly dynamic set of signals into functional information

Artificial Immune System Implementation for Predicting WM Presence from MYD88 and CXCR4

Waldenstrom’s Macroglobulinemia (WM) is a rare malignancy that affects human blood cells and spreads slowly. The development of WM occurs whenever the blood cells undergo genetic changes. Better therapies can be offered by the healthcare sector to get rid of the symptoms that cannot be cured. Everyone in the healthcare sector is aware that genetic abnormalities cause WM, but they are unsure of what causes the alterations. The risk factors that increase the number of WM's aberrant cells have been found. The greatest risk variables have a fatal impact on humans. The healthcare sector is working to save lives by offering better care. Only when WM is discovered earlier when it is treatable with better care and potent medications, is it very likely. For analysing the healthcare data associated with WM, a number of prior research studies have suggested both standard and unique software models and techniques. However, the accuracy is subpar and inefficient in terms of both time and money. To analyse the genomic dataset and detect Waldenstrom's Macroglobulinemia or its symptoms, this research explored this issue and suggested an Artificial Immune System (AIS) approach. Software written in Python is used to conduct the experiment and validate the findings. by contrasting the trial outcomes with other performance assessment techniques. The analysis reveals that the suggested AIS algorithm works better than the others

Desenvolvimentos de uma nova abordagem em inteligência artificial para deteção de anomalias

Doutoramento em Engenharia InformáticaEste trabalho visou o desenvolvimento do modelo de frustração celular para aplicações à segurança informática. Neste âmbito foram desenvolvidos os processos necessários para materializar o modelo de frustração celular num algoritmo semi-supervisionado de deteção de anomalias. É por seguida efetuada uma comparação da capacidade de discriminação do algoritmo de frustração celular com algoritmos do estado de arte, nomeadamente máquinas de vetores de suporte e florestas aleatórias (com sigla em inglês de SVM e RF, respetivamente). Verifica-se que nos casos estudados o algoritmo de frustração celular obtém uma capacidade de discriminação de anomalias semelhante, senão melhor, que os algoritmos anteriormente descritos. São ainda descritas otimizações para reduzir o elevado custo computacional do algoritmo recorrendo a novos paradigmas de computação, i.e. pelo uso de placas gráficas, assim como otimizações que visam reduzir a complexidade do algoritmo. Em ambos os casos foi verificada uma redução do tempo computacional. Por fim, é ainda verificado que as melhorias introduzidas permitiram que a capacidade de discriminação do algoritmo se tornasse menos sensível à perturbação dos seus parâmetros.This work sought to develop the cellular frustration model for computer security applications. In this sense, the required processes to materialize the cellular frustration model in a semi-supervised anomaly detection algorithm were developed. The discrimination capability of the cellular frustration algorithm was then compared with the discrimination capability of state of the art algorithms, namely support vector machines and random forests (SVMs and RFs, respectively). In the studied cases it is observed that the cellular frustration algorithm exhibits comparable, if not better, anomaly detection capabilities. Optimizations to reduce the high computational cost that rely on new computational paradigms, i.e. by the use of graphic cards, as well as optimizations to reduce the algorithm complexity were also described. In both cases it was observed a reduction of the computational time required by the algorithm. Finally, it was verified that the introduced improvements allowed the anomaly detection capability of the algorithm to become less sensitive to the perturbation of its parameters

A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

An Artificial Immune System Strategy for Robust Chemical Spectra Classification via Distributed Heterogeneous Sensors

The timely detection and classification of chemical and biological agents in a wartime environment is a critical component of force protection in hostile areas. Moreover, the possibility of toxic agent use in heavily populated civilian areas has risen dramatically in recent months. This thesis effort proposes a strategy for identifying such agents vis distributed sensors in an Artificial Immune System (AIS) network. The system may be used to complement electronic nose ( E-nose ) research being conducted in part by the Air Force Research Laboratory Sensors Directorate. In addition, the proposed strategy may facilitate fulfillment of a recent mandate by the President of the United States to the Office of Homeland Defense for the provision of a system that protects civilian populations from chemical and biological agents. The proposed system is composed of networked sensors and nodes, communicating via wireless or wired connections. Measurements are continually taken via dispersed, redundant, and heterogeneous sensors strategically placed in high threat areas. These sensors continually measure and classify air or liquid samples, alerting personnel when toxic agents are detected. Detection is based upon the Biological Immune System (BIS) model of antigens and antibodies, and alerts are generated when a measured sample is determined to be a valid toxic agent (antigen). Agent signatures (antibodies) are continually distributed throughout the system to adapt to changes in the environment or to new antigens. Antibody features are determined via data mining techniques in order to improve system performance and classification capabilities. Genetic algorithms (GAs) are critical part of the process, namely in antibody generation and feature subset selection calculations. Demonstrated results validate the utility of the proposed distributed AIS model for robust chemical spectra recognition

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.;The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.;Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.;The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.;This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.;The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems

Design and validation of structural health monitoring system based on bio-inspired algorithms

The need of ensure the proper performance of the structures in service has made of structural health monitoring (SHM) a priority research area. Researchers all around the world have focused efforts on the development of new ways to continuous monitoring the structures and analyze the data collected from the inspection process in order to provide information about the current state and avoid possible catastrophes. To perform an effective analysis of the data, the development of methodologies is crucial in order to assess the structures with a low computational cost and with a high reliability. These desirable features can be found in biological systems, and these can be emulated by means of computational systems. The use of bio-inspired algorithms is a recent approach that has demonstrated its effectiveness in data analysis in different areas. Since these algorithms are based in the emulation of biological systems that have demonstrated its effectiveness for several generations, it is possible to mimic the evolution process and its adaptability characteristics by using computational algorithms. Specially in pattern recognition, several algorithms have shown good performance. Some widely used examples are the neural networks, the fuzzy systems and the genetic algorithms. This thesis is concerned about the development of bio-inspired methodologies for structural damage detection and classification. This document is organized in five chapters. First, an overview of the problem statement, the objectives, general results, a brief theoretical background and the description of the different experimental setups are included in Chapter 1 (Introduction). Chapters 2 to 4 include the journal papers published by the author of this thesis. The discussion of the results, some conclusions and the future work can be found on Chapter 5. Finally, Appendix A includes other contributions such as a book chapter and some conference papers.La necesidad de asegurar el correcto funcionamiento de las estructuras en servicio ha hecho de la monitorización de la integridad estructural un área de gran interés. Investigadores en todas las partes del mundo centran sus esfuerzos en el desarrollo de nuevas formas de monitorización contínua de estructuras que permitan analizar e interpretar los datos recogidos durante el proceso de inspección con el objetivo de proveer información sobre el estado actual de la estructura y evitar posibles catástrofes. Para desarrollar un análisis efectivo de los datos, es necesario el desarrollo de metodologías para inspeccionar la estructura con un bajo coste computacional y alta fiabilidad. Estas características deseadas pueden ser encontradas en los sistemas biológicos y pueden ser emuladas mediante herramientas computacionales. El uso de algoritmos bio-inspirados es una reciente técnica que ha demostrado su efectividad en el análisis de datos en diferentes áreas. Dado que estos algoritmos se basan en la emulación de sistemas biológicos que han demostrado su efectividad a lo largo de muchas generaciones, es posible imitar el proceso de evolución y sus características de adaptabilidad al medio usando algoritmos computacionales. Esto es así, especialmente, en reconocimiento de patrones, donde muchos de estos algoritmos brindan excelentes resultados. Algunos ejemplos ampliamente usados son las redes neuronales, los sistemas fuzzy y los algoritmos genéticos. Esta tesis involucra el desarrollo de unas metodologías bio-inspiradas para la detección y clasificación de daños estructurales. El documento está organizado en cinco capítulos. En primer lugar, se incluye una descripción general del problema, los objetivos del trabajo, los resultados obtenidos, un breve marco conceptual y la descripción de los diferentes escenarios experimentales en el Capítulo 1 (Introducción). Los Capítulos 2 a 4 incluyen los artículos publicados en diferentes revistas indexadas. La revisión de los resultados, conclusiones y el trabajo futuro se encuentra en el Capítulo 5. Finalmente, el Anexo A incluye otras contribuciones tales como un capítulo de libro y algunos trabajos publicados en conferencias

Population Physiology, Demography, and Genetics of Side-Blotched Lizards (\u3cem\u3eUta stansburiana\u3c/em\u3e) Residing in Urban and Natural Environments

Wildlife populations across the globe are poised to lose their natural habitat to urbanization, yet there is limited information on how different species handle living in cities. Animals in urban environments are often susceptible to novel stressors, which can threaten their individual health and population viability. The physiological characteristics of animals, such as those related to metabolic hormones, oxidative stress, and immunity, are expected to be important for survival in this context. If so, animals persisting in urban areas may demonstrate physiological differences from their natural counterparts, perhaps due to evolutionary change. These potential outcomes have been documented in birds and mammals, but other taxonomic groups such as reptiles have been studied far less. For this dissertation, lizards were sampled in urban and natural areas for six years to (i) compare annual population survival, (ii) identify physiological traits important for survival, (iii) map the genetic basis of these traits, and (iv) test if and how the physiological traits are evolving in urban environments. Lizard survival was lower in urban environments and related to differences in immunity. Each physiological trait had a low to moderate heritable basis linked to few genetic loci with measurable effects. Population-level genetic comparisons revealed lizards in urban areas to be differentiated from those residing in natural areas, though shared genetic variation was present among populations along with comparable levels of genetic diversity. Differential selective pressures on the traits and their associated genetic loci were not detected, but indicators of genetic drift were evident across the landscape. Altogether, these findings shed light on the interconnectedness of population demography, physiology, and genetics for reptiles residing in urban environments

Individual-based modeling and predictive simulation of fungal infection dynamics

The human-pathogenic fungus Aspergillus fumigatus causes life-threatening infections in immunocompromised patients and poses increasing challenges for the modern medicine. A. fumigatus is ubiquitously present and disseminates via small conidia over the air of the athmosphere. Each human inhales several hundreds to thousands of conidia every day. The small size of conidia allows them to pass into the alveoli of the lung, where primary infections with A. fumigatus are typically observed. In alveoli, the interaction between fungi and the innate immune system of the host takes place. This interaction is the core topic of this thesis and covered by mathematical modeling and computer simulations. Since in vivo laboratory studies of A. fumigatus infections under physiological conditions is hard to realize a modular software framework was developed and implemented, which allows for spatio-temporal agent-based modeling and simulation. A to-scale A. fumigatus infection model in a typical human alveolus was developed in order to simulate and analyze the infection scenario under physiological conditions. The process of conidial discovery by alveolar macrophages was modeled and simulated with different migration modes and different parameter configurations. It could be shown that chemotactic migration was required to find the pathogen before the onset of germination. A second model took advantage of evolutionary game theory on graphs. Here, the course of infection was modeled as a consecutive sequence of evolutionary games related to the complement system, alveolar macrophages and polymorphonuclear neutrophilic granulocytes. The results revealed a central immunoregulatory role of alveolar macrophages. In the case of high infectious doses it was found that the host required fully active phagocytes, but in particular a qualitative response of quantitatively sufficient polymorphonuclear neutrophilic granulocytes.Der human-pathogene Schimmelpilz Aspergillus fumigatus verursacht tödliche Infektionen und Erkrankungen vorrangig bei immunsupprimierten Patienten und stellt die moderne Medizin vor zunehmende Herausforderungen. A. fumigatus ist ubiquitär präsent und verbreitet sich über sehr kleine Konidien durch Luftströmungen in der Athmosphäre. Mehrere Hundert bis Tausende dieser Konidien werden täglich durch jeden Menschen eingeatmet. Die geringe Größe der infektiösen Konidien erlauben es dem Pilz bis in die Alveolen der Lunge des Wirtes vorzudringen,in denen eine Primärinfektionen mit A. fumigatus am häufigsten stattfindet. Die Alveolen sind der zentrale Schauplatz der Interaktion zwischen dem Pilz und dem angeborenen Immunsystem, welche Gegenstand dieser Arbeit ist. Diese Interaktion wird mit Hilfe von mathematischen Modellen und Computersimulationen nachgestellt und untersucht, da eine A. fumigatus Infektion im Nasslabor in vivo unter physiologischen Bedingungen nur sehr schwer realisiert werden kann. Als Grundlage für dieses Vorhaben wurde ein modulares Software-Paket entwickelt, welches agentenbasierte Modellierung und entsprechende Simulationen in Raum und Zeit ermöglicht. Ein maßstabsgetreues mathematisches Infektionsmodell in einer typischen menschlichen Alveole wurde entwickelt und die Suchstrategien von Alveolarmakrophagen unter der Berücksichtigung verschiedener Parameter wie Migrationsgeschwindigkeit, dem Vorhandensein von Chemokinen, dessen Diffusion und Chemotaxis untersucht. Es zeigte sich, dass Chemotaxis, notwendig ist, um die Konidie rechtzeitig finden zu können. In einem weiteren Modell, welches auf das Konzept evolutionärer Spieltheorie auf Graphen zurückgegriff, wurde der Infektionsverlauf als aufeinanderfolgende Serie evolutionärer Spiele mit dem Komplementsystem, Alveolarmakrophagen und Neutrophilen nachgestellt. Aus den Simulationsergebnissen konnte eine zentrale immunregulatorische Rolle von Alveolarmakrophagen entnommen werden