Vários problemas do mundo real podem ser analisados como problemas de otimização. Na bioinformática, em especial, como exemplos podem ser citados o alinhamento múltiplo de sequências, a filogenia, a predição de estruturas de proteínas e RNA, entre outros. As Meta-heurísticas Populacionais (MhP) são técnicas baseadas em interações de conjuntos de soluções candidatas, como elementos de uma população, utilizadas na otimização de funções. Seu uso é especialmente interessante na otimização de problemas onde há conhecimento parcial ou nenhum do espaço de busca. O objetivo deste trabalho é investigar o uso de auto-organização da população de um sistema imunológico artificial (AIS) a fim de aplicá-lo no problema de docking, que pode ser visto como um problema de otimização multimodal complexo. O AIS é um tipo de MhP inspirado na microevolução do sistema imunológico adaptativo de organismos complexos. Neste, as soluções candidatas representam células do sistema imunológico que busca se adaptar para a eliminação de um patógeno. O desenvolvimento do algoritmo foi baseado no opt-aiNet, que utiliza dos princípios das teorias de seleção clonal e maturação de afinidade para realizar a otimização de funções. Adicionalmente, o opt-aiNet, inspirado na teoria de redes imunológicas, realiza uma etapa de supressão, que busca eliminar soluções semelhantes, aumentando assim a diversidade populacional. Esta etapa é computacionalmente custosa, dado que é feito o cálculo da distância entre todos os possíveis pares de células (soluções) afim de eliminar aquelas próximas de acordo com um dado critério. A proposta deste trabalho é o desenvolvimento de um algoritmo de supressão auto-organizável, inspirado no fenômeno da criticalidade auto-organizada, buscando diminuir a influência da seleção de parâmetros e a complexidade da etapa de supressão. O algoritmo proposto foi testado em um conjunto de funções contínuas conhecidas e comumente utilizadas pela comunidade de computação evolutiva. Os resultados obtidos foram comparados com aqueles de uma implementação do opt-aiNet. Em adição, foi proposta a utilização de operadores de mutação com distribuição q-gaussiana nos AISs desenvolvidos. O algoritmo foi também aplicado no problema de docking rígido baseado em complementaridade de superfícies e minimização de colisões, especificamente no docking de proteínas. Os resultados foram comparados com aqueles de um algoritmo genético, resultando em um melhor desempenho obtido pelo algoritmo proposto. / Many real world problems can be described as optimization problems. In bioinformatics in special, there is multiple sequence alignment, filogeny and RNA and Protein structure prediction, among others. Population based metaheuristics are techniques based in the interaction of a set of candidate solutions as elements of a population. Its use is specially interesting in optimization problems where there is little or no knowledge of the search space. The objective of this work is to study the use of self-organization of population in an artificial imune system for use in the docking problem, considered a complex multimodal optimization problem. The artificial imunme system is a type of population based methaheuristics inspired in the microevolution of the adaptive immune system of complex organisms. Candidate solutions represent cells of the immune system adapting its antibodies to eliminate a pathogen. The development of the algorithm was based in the opt-aiNet, based in the principles of clonal selection and affinity maturation for function optimization. Additionally, the opt-aiNet, inspired in theories of immune network, makes a suppression stage to eliminate similiar solutions and control diversity. This stage is computationally expensive as it calculates the distance between every possible pair of cells (solutions) eliminating those closer than a threshold. This work proposes a self-organized suppression algorithm inspired in the self-organized criticality, looking to minimize the influence of parameter selection and complexity of the suppression stage in opt-aiNet. The proposed algorithm was tested in a set of well-known functions in the evolutionary computation community. The results were compared to those of an implementation of the opt-aiNet. In addition, we proposed a mutation operator with q-Gaussian distribution for the artificial immune systems. The algorithm was then applied in the rigid protein docking problem based in surface complementarity and colision avoidance. The results were compared with a genetic algorithm and achieved a better performance.

Artificial immune systems (AIS), biologically inspired from natural immune functions, can be reactive as well as adaptive in handling generic and varying pathogens, respectively. Researchers have used the immunological metaphors to solve science and engineering problems where unknown/unexpected scenarios are plausible. AIS can be a suitable choice for various robotic applications requiring reactive and/or deliberative control. This research aims to translate modern trends in immunology, to develop an immunity-based framework, to control a team of heterogenous robots on varying levels of task allocation and mutual interactions. The presented framework is designed to work as a multi-agent system in which safe environment is treated reactively through innate immunity, whereas unsafe situations invoke adaptive part of immune system, simultaneously. Heterogeneity is defined in terms of different sensing and/or actuation capabilities as well as in terms of different behavior-sets robot(s) possess. Task allocation ranges from primitive to advanced behaviors. Mutual interactions, on the other hand, range from simpler one-to-one interaction to mutual coordination. In this context, a new immunity-based algorithm has been developed & tested, combining innate and adaptive immunities, to regulate cell populations and corresponding maturations, along with internal health indicators, in order to effectively arbitrate behaviors/robots in a heterogenous robotic system, in environments that are dynamic and unstructured. / text

Diss. (sammanfattning) Linköping : Univ., 2003. / Härtill 4 uppsatser.

CD4+ T cells play a central role in immunity. During virus infections, CD4+ T cells provide the necessary help for B cells to secrete anti-viral antibody and may act as effector cells themselves through the secretion of anti-viral cytokines such as IFN-γ and TNF-α. Recent studies in the lymphocytic choriomeningitis virus (LCMV) system have shown that CD4+ T cells are required to maintain the clearance of persistent viral infections as well as maintain virus-specific memory CD8+ cytotoxic T lymphocytes (CTL). Despite these important functions, surprisingly little information exists concerning the longevity, magnitude, and stability of the CD4+ T cell response following a virus infection. This thesis takes advantage of the well-studied LCMV system to address the above issues as well as to examine the role CD4+ T cells play during heterologous virus infections and to determine the fate of CD4+T cells following a high-dose LCMV infection. The cell surface phenotype of the CD4+ T cells was first examined in C57BL/6 mice acutely infected with LCMV. FACS analysis revealed the modulation of several activation markers on CD4+ T cells during an acute infection with LCMV, consistent with an activated cell phenotype. In addition, 25% of the CD4+ T cells were blast-sized by day 7 post-infection (p.i.) even though the total number of CD4+ T cells did not increase in the spleen during the acute infection. Additional studies were performed using CZ-1, a novel monoclonal antibody (mAb) previously generated in our laboratory that defines a sialic acid-dependent CD45RB-associated epitope. Examination of the expression of the CZ-1 antigen on CD4+ T cells following LCMV infection revealed that the blast-sized CD4+ T cells at day 6 p.i. were CZ-1 +. Further cell surface phenotyping showed that those blast cells activated at day 6 p.i. were CD45RB1oCD44hiCD62L-. This contrasts with the CZ-1-CD45RBhiCD441oCD62L+ resting cell population prior to infection. To determine if memory CD4+ T cells continued to express the CZ-1 epitope long after resolution of the LCMV infection, CD4+CZ-1+ and CD4+CZ-1- populations were purified by cell sorting and placed into an in vitro proliferation assay with LCMV-infected antigen-presenting cells (APC). It was found that the CD4+CZ-1+ population contained virtually all of the virus-specific memory. Thus, these studies indicate that the CZ-1 epitope defines a novel activation and memory marker for murine CD4+T cells. Examination of virus-specific cytokine production using ELISPOT assays showed a significant increase in the number of IFN-γ-secreting cells in the spleen during an acute LCMV-infection. CD8+ T cells made up the majority of the IFN-γ-producing cells, but analysis of the cell culture supernatants by ELISA revealed that the CD4+T cells produced more IFN-γ on a per cell basis. No significant increase in IL-4 levels was detected under these experimental conditions. These data suggest that LCMV infection induces primarily a virus-specific Th1 response that is characterized by increased IFN-γ production. No quantitative information was known about the frequency and longevity of the LCMV-specific CD4+ T cell response. Using limiting dilution assays (LDA), I examined the CD4+ T cell precursor (Thp) frequency in C57BL/6 mice infected with LCMV. The virus-specific CD4+ Thp frequency increased from <1/100,000 in uninfected mice to a peak of approximately 1/600 in FACS-purified splenic CD4+ T cell populations by 10 days p.i. with LCMV. After the peak of the response, the CD4+ Thp frequency decreased only about 2-fold per CD4+ T cell to approximately 1/1200 and remained stable into long-term memory. The CD4+ Thp frequency to each of the two known LCMV major histocompatibility complex (MHC) class II-restricted peptides dropped only 2- to 7-fold from the peak of the acute LCMV response into long-term memory. Thus, the CD4+T cell frequencies remain elevated after the acute infection subsides and remain extremely stable throughout long-term immunity. The above results show that LDA can account for +T cells as being virus-specific following LCMV infection. However, using newer, more sensitive assays based on intracellular cytokine production, >20% of the CD4+ T cells secreted IFN-γ after stimulation with phorbol myristic acid and ionomycin during the peak of the acute CD4+ T cell response. In addition, >10% of the CD4+ T cells secreted IFN-γ after stimulation with the LCMV MHC class II-restricted CD4 peptides. Thus, these new sensitive assays reveal a heretofore unappreciated, yet profound antigen-specific CD4+T cell response during LCMV infection. Infection of mice with a series of unrelated viruses, termed heterologous viruses, causes the reduction of memory CD8+ T cells specific to earlier infections. In order to examine the fate of CD4+ T cells under these conditions, I examined cytokine production and followed the CD4+ Thp frequency following heterologous virus infections. Challenge of LCMV-immune mice with vaccinia virus (VV) resulted in a significant increase in both the amount of IFN-γ protein and the frequency of IFN-γ-producing cells in the peritoneal cavity 3 days after infection as compared to control non-immune mice acutely infected with VV or to LCMV-immune mice alone. Intracellular IFN-γ staining revealed that both CD4+ and CD8+ T cells contributed to this increased IFN-γ production. LDA analysis of the LCMV-specific CD4+ Thp frequency following multiple heterologous virus infections or protein antigen immunizations, revealed that the CD4+ Thp frequency remains stable even under conditions that reduce the LCMV-specific CD8+ CTLp frequency. Additional studies using high-dose LCMV Clone 13 demonstrated that, like CD8+ T cells, there is a decline in detectable LCMV-specific CD4+Thp during overwhelming virus infections. The data presented in this thesis help provide a better understanding of the CD4+ T cell response during virus infections. I make several novel observations, including the demonstration that mAb CZ-1 defines a novel activation and memory marker for CD4+ T cells, that the LCMV-specific memory CD4+ Thp frequency remains extremely stable into long-term immunity, and that heterologous virus infections do not disturb the stable memory CD4+ T cell pool following a virus infection. I also provide data using new sensitive assays based on intracellular cytokine production that there is a much more profound antigen-specific CD4+ T cell response during viral infections than has previously been realized. Finally, I provide evidence that the virus-specific CD4+ T cells become unresponsive following a high-dose LCMV Clone 13 infection. Thus, the data presented in this thesis highlight some important similarities and differences between the CD4+ and CD8+ T cell responses during acute viral infections.

Neste trabalho é analisada a aplicação da técnica de Sistemas Imunológicos Artificiais (SIA) a um problema do mundo real: como predizer fraudes e furtos de energia elétrica. Vários trabalhos tem mostrado que épossível detectar padrões de dados anormais a partir dos dados de consumidores de energia elétrica e descobrir problemas como fraude e furto. Sistemas Imunológicos Artificiais é um ramo recente da Inteligência Computacional e tem diversas possíveis aplicações, sendo uma delas o reconhecimento de padrões. Mais de um algoritmo pode ser empregado para criar um SIA; no escopo deste trabalho será empregado o algoritmo Clonalg. A eficácia deste algoritmo é medida e comparada com a de outros métodos de classificação. A amostra de dados usada para validar este trabalho foi fornecida por uma companhia de energia elétrica. Os dados fornecidos foram selecionados e transformados com o objetivo de eliminar redundância e normalizar valores. / In this paper, we analyze the application of an Artificial Immune System (AIS) to a real world problem: how to predict electricity fraud and theft. Various works have explained that it is possible to detect abnormal data patterns from electricity consumers and discover problems like fraud and theft. Artificial Immune Systems is a recent branch of Computational Intelligence and has several possible applications, one of which is pattern recognition. More than one algorithm can be employed to create an AIS; we selected the Clonalg algorithm for our analysis. The efficiency of this algorithm is measured and compared with that of other classifier methods. The data sample used to validate this work was provided by an electrical energy company. The provided data were selected and transformed with the aim of eliminating redundant data and to normalize values.

Neste trabalho é analisada a aplicação da técnica de Sistemas Imunológicos Artificiais (SIA) a um problema do mundo real: como predizer fraudes e furtos de energia elétrica. Vários trabalhos tem mostrado que épossível detectar padrões de dados anormais a partir dos dados de consumidores de energia elétrica e descobrir problemas como fraude e furto. Sistemas Imunológicos Artificiais é um ramo recente da Inteligência Computacional e tem diversas possíveis aplicações, sendo uma delas o reconhecimento de padrões. Mais de um algoritmo pode ser empregado para criar um SIA; no escopo deste trabalho será empregado o algoritmo Clonalg. A eficácia deste algoritmo é medida e comparada com a de outros métodos de classificação. A amostra de dados usada para validar este trabalho foi fornecida por uma companhia de energia elétrica. Os dados fornecidos foram selecionados e transformados com o objetivo de eliminar redundância e normalizar valores. / In this paper, we analyze the application of an Artificial Immune System (AIS) to a real world problem: how to predict electricity fraud and theft. Various works have explained that it is possible to detect abnormal data patterns from electricity consumers and discover problems like fraud and theft. Artificial Immune Systems is a recent branch of Computational Intelligence and has several possible applications, one of which is pattern recognition. More than one algorithm can be employed to create an AIS; we selected the Clonalg algorithm for our analysis. The efficiency of this algorithm is measured and compared with that of other classifier methods. The data sample used to validate this work was provided by an electrical energy company. The provided data were selected and transformed with the aim of eliminating redundant data and to normalize values.

Neste trabalho é analisada a aplicação da técnica de Sistemas Imunológicos Artificiais (SIA) a um problema do mundo real: como predizer fraudes e furtos de energia elétrica. Vários trabalhos tem mostrado que épossível detectar padrões de dados anormais a partir dos dados de consumidores de energia elétrica e descobrir problemas como fraude e furto. Sistemas Imunológicos Artificiais é um ramo recente da Inteligência Computacional e tem diversas possíveis aplicações, sendo uma delas o reconhecimento de padrões. Mais de um algoritmo pode ser empregado para criar um SIA; no escopo deste trabalho será empregado o algoritmo Clonalg. A eficácia deste algoritmo é medida e comparada com a de outros métodos de classificação. A amostra de dados usada para validar este trabalho foi fornecida por uma companhia de energia elétrica. Os dados fornecidos foram selecionados e transformados com o objetivo de eliminar redundância e normalizar valores. / In this paper, we analyze the application of an Artificial Immune System (AIS) to a real world problem: how to predict electricity fraud and theft. Various works have explained that it is possible to detect abnormal data patterns from electricity consumers and discover problems like fraud and theft. Artificial Immune Systems is a recent branch of Computational Intelligence and has several possible applications, one of which is pattern recognition. More than one algorithm can be employed to create an AIS; we selected the Clonalg algorithm for our analysis. The efficiency of this algorithm is measured and compared with that of other classifier methods. The data sample used to validate this work was provided by an electrical energy company. The provided data were selected and transformed with the aim of eliminating redundant data and to normalize values.

Vários problemas do mundo real podem ser analisados como problemas de otimização. Na bioinformática, em especial, como exemplos podem ser citados o alinhamento múltiplo de sequências, a filogenia, a predição de estruturas de proteínas e RNA, entre outros. As Meta-heurísticas Populacionais (MhP) são técnicas baseadas em interações de conjuntos de soluções candidatas, como elementos de uma população, utilizadas na otimização de funções. Seu uso é especialmente interessante na otimização de problemas onde há conhecimento parcial ou nenhum do espaço de busca. O objetivo deste trabalho é investigar o uso de auto-organização da população de um sistema imunológico artificial (AIS) a fim de aplicá-lo no problema de docking, que pode ser visto como um problema de otimização multimodal complexo. O AIS é um tipo de MhP inspirado na microevolução do sistema imunológico adaptativo de organismos complexos. Neste, as soluções candidatas representam células do sistema imunológico que busca se adaptar para a eliminação de um patógeno. O desenvolvimento do algoritmo foi baseado no opt-aiNet, que utiliza dos princípios das teorias de seleção clonal e maturação de afinidade para realizar a otimização de funções. Adicionalmente, o opt-aiNet, inspirado na teoria de redes imunológicas, realiza uma etapa de supressão, que busca eliminar soluções semelhantes, aumentando assim a diversidade populacional. Esta etapa é computacionalmente custosa, dado que é feito o cálculo da distância entre todos os possíveis pares de células (soluções) afim de eliminar aquelas próximas de acordo com um dado critério. A proposta deste trabalho é o desenvolvimento de um algoritmo de supressão auto-organizável, inspirado no fenômeno da criticalidade auto-organizada, buscando diminuir a influência da seleção de parâmetros e a complexidade da etapa de supressão. O algoritmo proposto foi testado em um conjunto de funções contínuas conhecidas e comumente utilizadas pela comunidade de computação evolutiva. Os resultados obtidos foram comparados com aqueles de uma implementação do opt-aiNet. Em adição, foi proposta a utilização de operadores de mutação com distribuição q-gaussiana nos AISs desenvolvidos. O algoritmo foi também aplicado no problema de docking rígido baseado em complementaridade de superfícies e minimização de colisões, especificamente no docking de proteínas. Os resultados foram comparados com aqueles de um algoritmo genético, resultando em um melhor desempenho obtido pelo algoritmo proposto. / Many real world problems can be described as optimization problems. In bioinformatics in special, there is multiple sequence alignment, filogeny and RNA and Protein structure prediction, among others. Population based metaheuristics are techniques based in the interaction of a set of candidate solutions as elements of a population. Its use is specially interesting in optimization problems where there is little or no knowledge of the search space. The objective of this work is to study the use of self-organization of population in an artificial imune system for use in the docking problem, considered a complex multimodal optimization problem. The artificial imunme system is a type of population based methaheuristics inspired in the microevolution of the adaptive immune system of complex organisms. Candidate solutions represent cells of the immune system adapting its antibodies to eliminate a pathogen. The development of the algorithm was based in the opt-aiNet, based in the principles of clonal selection and affinity maturation for function optimization. Additionally, the opt-aiNet, inspired in theories of immune network, makes a suppression stage to eliminate similiar solutions and control diversity. This stage is computationally expensive as it calculates the distance between every possible pair of cells (solutions) eliminating those closer than a threshold. This work proposes a self-organized suppression algorithm inspired in the self-organized criticality, looking to minimize the influence of parameter selection and complexity of the suppression stage in opt-aiNet. The proposed algorithm was tested in a set of well-known functions in the evolutionary computation community. The results were compared to those of an implementation of the opt-aiNet. In addition, we proposed a mutation operator with q-Gaussian distribution for the artificial immune systems. The algorithm was then applied in the rigid protein docking problem based in surface complementarity and colision avoidance. The results were compared with a genetic algorithm and achieved a better performance.

This study was an investigation of the effects of physiologically-oriented mental imagery on immune functioning. College students with normal medical histories were randomly selected to one of three groups. Subjects in Group 1 participated in short educational training on the production of secretory immunoglobulin A. They were then tested on salivary IgA, skin temperature and the Profile of Mood States (POMS) before and after listening to a 17-minute tape of imagery instructions with specially-composed background "entrainment" music, designed to enhance imagery. Subjects in Group 2 (placebo controls) listened to the same music but received no formal training on the immune system. Group 3 acted as a control and subjects were tested before and after 17 minutes of no activity. Treatment groups listened to their tapes at home on a bi-daily basis for six weeks. All groups were again tested at Weeks 3 and 6. Secretory IgA was analyzed using standard radial immuno-diffusion techniques. Repeated measures analyses of variance with planned orthogonal contrasts were used to evaluate the data. Significant overall increases (p < .05) were found between pre- and posttests for all three trials. Groups 1 and 2 combined (treatment groups) yielded significantly greater increases in slgA over Group 3 (control) for all three trials. Group 1 (imagery) was significantly higher than Group 2 (music) in antibody production for Trials 2 and 3. No group differences were noted in saliva volume or skin temperature, indicating that autonomic physiological mechanisms were not responsible for differences in antibody production. POMS changes more often favored Group 1. Symptomatology, recorded by subjects at weeks three and six, was significantly lower for three symptoms (rapid heartbeat, breathing difficulty, and jaw clenching), again favoring both treatment groups over the control group. Conclusions were that CNS-mediated immunoenhancement through mental imagery is possible.

Glia, whose name derives from the original Greek word, meaning “glue,” have long been understood to be cells that play an important functional role in the nutritive and structural support of the central nervous system, yet their full involvement has been historically undervalued. Despite the strong evidence that glial reactions to cellular debris govern the health of the nervous system, the specific properties of damaged axonal debris and the mechanisms by which glia sense them, morphologically adapt to their presence, and initiate phagocytosis for clearance, have remained poorly understood. The work presented in this thesis was aimed at addressing this fundamental gap in our understanding of the role for glia in neurodegenerative processes. I demonstrate that the cellular machinery responsible for the phagocytosis of apoptotic cell corpses is well conserved from worms to mammals. Draper is a key component of the glial response machinery and I am able to show here, for the first time, that it signals through Drosophila Shark, a non-receptor tyrosine kinase similar to mammalian Syk and Zap-70. Shark binds Draper through an immunoreceptor tyrosine-based activation motif (ITAM) in the Draper intracellular domain. I show that Shark activity is essential for Draper-mediated signaling events in vivo, including the recruitment of glial membranes to axons undergoing Wallerian degeneration. I further show that the Src family kinase (SFK) Src42A can markedly increase Draper phosphorylation and is essential for glial phagocytic activity. Therefore I propose that ligand-dependent Draper receptor activation initiates the Src42A-dependent tyrosine phosphorylation of Draper, the association of Shark and the subsequent downstream activation of the Draper pathway. I observed that these Draper-Src42A-Shark interactions are strikingly similar to mammalian immunoreceptor-SFK-Syk signaling events in myeloid and lymphoid cells. Thus, Draper appears to be an ancient immunoreceptor with an extracellular domain tuned to modified-self antigens and an intracellular domain that promotes phagocytosis through an ITAM domain-SFK-Syk-mediated signaling cascade. I have further identified the Drosophila guanine-nucleotide exchange factor (GEF) complex Crk/Mbc/dCed-12, and the small GTPase Rac1 as novel modulators of glial clearance of axonal debris. I am able to demonstrate that Crk/Mbc/dCed-12 and Rac1 function in a non-redundant fashion with the Draper pathway to promote a distinct step in the clearance of axonal debris. Whereas Draper signaling is required early during glial responses, promoting glial activation and extension of glial membranes to degenerating axons, the Crk/Mbc/dCed-12 complex functions at later stages of glial response, promoting the actual phagocytosis of axonal debris. Finally, many interesting mutants have been identified in primary screens for genes active in neurons that are required for axon fragmentation or clearance by glia, and genes potentially active in glia that orchestrate clearance of fragmented axons. The further characterization of these genes will likely unlock the mystery surrounding “eat me” and “find me” cues hypothesized to be released or exposed by neurons undergoing degeneration. Illuminating these important glial pathways could lead to a novel therapeutic approach to brain trauma or other neurodegenerative conditions by providing a druggable means of inducing early attenuation of the glial response to injury down to levels less damaging to the brain. Taken together, my combined work identifies new components of the glial engulfment machinery and shows that glial activation, phagocytosis of axonal debris, and the termination of glial responses to injury are genetically separable events mediated by distinct signaling pathways.