CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / O fator de elongaÃÃo 1alfa (EF1α) à responsÃvel pela ligaÃÃo do aminoacil-tRNA ao sÃtio A do ribossomo, atuando, assim, na fase de alongamento da sÃntese proteica. O EF1α em plantas à codificado por uma famÃlia multigÃnica com nÃmero de genes variÃvel entre espÃcies. O objetivo do presente trabalho foi caracterizar, analisar filogeneticamente e elucidar o perfil de expressÃo dos genes EF1α em soja durante o desenvolvimento, bem como selecionar o(s) gene(s) de referÃncia mais estÃveis para ensaios de qRT-PCR no desenvolvimento e condiÃÃes de estresse. AnÃlises in silico nos genomas de soja e de outras leguminosas disponÃveis em bancos de dados revelaram famÃlias gÃnicas de 3 a 6 membros com estrutura conservada de 2 Ãxons e 2 Ãntrons. Dentre as espÃcies analisadas (Glycine max, Cajanus cajan, Vigna unguiculata, Medicago truncatula e Phaseolus vulgaris) G. max foi a que apresentou o maior nÃmero de genes, classificados como EF1α 1a; 1b; 1c; 2a; 2b e 3 apÃs anÃlise filogenÃtica. A expressÃo dos genes EF1α foi estudada em diferentes tecidos (flores, vagens, sementes, cotilÃdones, folhas unifolioladas e trifolioladas, raÃzes, hipocÃtilos e epicÃtilos) durante o desenvolvimento da soja. A anÃlise de expressÃo por qPCR revelou que os genes EF1α sÃo funcionais, sendo expressos em todos os tecidos, contudo apresentam expressÃo diferencial dependente do tecido e do estÃdio de desenvolvimento. A expressÃo relativa entre os genes membros mostrou predomÃnio de expressÃo para 5 dos 6 genes analisados: EF1α 1a: sem predomÃnio de expressÃo; EF1α 1b: GerminaÃÃo e desenvolvimento do hipocÃtilo; EF1α 1c: Desenvolvimento da vagem; EF1α 2a: Desenvolvimento da vagem, folhas unifoliolada e trifoliolada; EF1α 2b: Desenvolvimento da vagem, folhas unifolioladas; EF1α 3: GerminaÃÃo, flores, raÃzes, fases iniciais do desenvolvimento do hipocÃtilo e epicÃtilo. Esses dados, juntamente com os de folhas e de raÃzes submetidas a condiÃÃes de estresse (PEG e AS) foram usados para avaliar a estabilidade de expressÃo dos diferentes genes EF1α de soja atravÃs dos programas GeNorm e NormFinder. Para as anÃlises durante o desenvolvimento, quatro genes de expressÃo estÃvel (UKN1, SKIP16, EF1β e MTP) foram tambÃm utilizados. Os genes EF1α foram mais estÃveis em cotilÃdones (EF1α 3 e EF1α 1c); epicÃtilos (EF1α 1b, EF1α 2b e EF1α 1a); hipocÃtilos (EF1α 1a e EF1β); vagens (EF1α 2a e EF1α 2b) e raÃzes (EF1α 2a e UKN1) e menos estÃveis em folhas trifolioladas/unifolioladas (UKN1 e SKIP16) e sementes em germinaÃÃo (EF1β e SKIP16). Em folhas sob condiÃÃes de estresse, anÃlises atravÃs do GeNorm revelaram que quatro genes (EF1α 2a >1b >2b >1a) apresentaram classificaÃÃo de estabilidade semelhante nos estresses por AS e PEG. Jà em raÃzes, distinto padrÃo de estabilidade foi encontrado: EF1α 2a > 1c >2b >1b > 1a >3 para PEG e EF1α 1c > 3 > 2a > 1a > 2b > 1b para AS. Tais resultados foram confirmados usando o programa NormFinder. A despeito das variaÃÃes de expressÃo gÃnica em diferentes tecidos em desenvolvimento e condiÃÃes de estresse, foram determinados genes EF1α que podem ser usados para normalizar ensaios de PCR em tempo real em soja e leguminosas (tribo Phaseoleae) vez que genes EF1α ortÃlogos foram identificados entre essas diferentes espÃcies. / The elongation factor 1alpha (EF1α) is responsible for binding of aminoacyl-tRNA to the A site of the ribosome, acting thus the elongation phase of protein synthesis. The EF1α in plants is encoded by a multigene family with variable number of genes among species. The aim of this study was to characterize, analyze phylogenetic and elucidate the expression profile of EF1α genes in soybean during development, as well as select (s) gene (s) of the most stable reference for qRT-PCR assays in the development and conditions stress. In silico analyses in the genomes of soybean and other leguminous plants available in databases revealed gene families 3-6 members with conserved structure of 2 exons and 2 introns. Among the analyzed species (Glycine max, Cajanus cajan, Vigna unguiculata, Phaseolus vulgaris and Medicago truncatula) G. max was the one with the largest number of genes classified as EF1α 1a, 1b, 1c, 2a, 2b and 3 after phylogenetic analysis. The EF1α gene expression was studied in different tissues (flowers, pods, seeds, cotyledons, trifoliate and unifolioladas leaves, roots, hypocotyls and epicotyls) during the development of soybean. Expression analyses by qPCR revealed that the EF1α genes are functional and expressed in all tissues, however exhibit differential expression dependent on the tissue and developmental stage. Relative expression between genes members showed predominance of expression for 5 of the 6 genes analyzed: EF1α 1a: no predominance of expression; EF1α 1b: Germination and development hypocotyl; EF1α 1c: pods development; EF1α 2a: pods development, unifoliate and trifoliate leaves; EF1α 2b: pods development, unifoliate leaves; EF1α 3: Germination, flowers, roots, early stages development (hypocotyl and epicotyl). These data, together with the leaves and roots subjected to stress conditions (PEG and AS) were used to assess the stability of expression of different EF1α genes soybean through the programs GeNorm and NormFinder. For the analyses during development, four stable genes (UKN1, SKIP16, EF1β and MTP) were also used. EF1α genes were more stable in cotyledons (EF1α 3 and EF1α 1c); epicotyls (EF1α 1b, 2b and 1a), hypocotyls (EF1α 1a, EF1β and EF1α 1c); pods (EF1α 2a and EF1α 2b) and roots (EF1α 2b and UKN1) and less stable in trifoliate/unifoliolate leaves (UKN1 and SKIP16) and germinating seeds (EF1β and SKIP16). In leaves under stress conditions, through GeNorm analyses revealed that four genes (EF1α 2a> 1b> 2b> 1a) showed similar stability in the classification of stress for AS and PEG. Already in roots, distinct pattern of stability was found: EF1α 2a> 1c> 2b> 1b> 1a> 3 for PEG and EF1α 1c> 3> 2a> 1a> 2b> 1b to AS. These results were confirmed using the program NormFinder. In spite of changes in gene expression in different tissues in development and stress conditions were determined EF1α genes that can be used to normalize the qPCR assays in soybean and others leguminous plants (Phaseoleae tribe) seeing that EF1α genes orthologous were identified between these different species.

Ces travaux abordent la problématique du contrôle endogène dans les Systèmes Multi-Agents (SMA) pour la résolution de problèmes complexes, que nous explorons grâce au problème de partage de ressources critiques. Les problèmes dits complexes que nous abordons se caractérisent par une explosion combinatoire du nombre de solutions avec la taille des problèmes, une forte dynamique des données du problème induite par un environnement ouvert dans lequel de nombreux événements peuvent avoir lieu, une grande complexité systémique issue des interdépendances entre les nombreuses variables du problèmes et enfin une décentralisation du processus de résolution imposée par une distribution physique et fonctionnelle des variables incompatible avec une vision centralisée du problème. Un parcours complet des espaces de recherche associés à de tels problèmes est irréaliste en un temps acceptable, il est alors nécessaire d'employer des méthodes de résolution dite incomplètes. Quelque soit l'approche incomplète considérée, le parcours incomplet de l'espace de recherche requiert un contrôle afin de maximiser la probabilité de converger vers une solution satisfaisante. Nous identifions trois niveaux de contrôle du parcours de l'espace de recherche indépendamment de l'approche utilisée : un contrôle statique (définition a priori du comportement du système), un contrôle dynamique (évoluant en cours de résolution selon des mécanismes préétablis) et un contrôle adaptatif (évoluant dynamiquement en cours de résolution). Nous montrons qu'un contrôle endogène de l'activité du système, c.-à-d. un contrôle adaptatif issu de l'activité même des agents, est nécessaire au guidage du parcours de l'espace de recherche dans le contexte de résolution de problèmes complexes. Ces travaux ayant été réalisés dans un contexte de collaboration industrielle, ils s'appuient sur une approche développée au cours de précédents travaux : CESNA (Complex Exchanges between Stigmergic Negotiating Agents). CESNA est une approche multi-agents auto-organisationnelle exploitant des agents situés dans un environnement matérialisant le problème et exploité par un processus de résolution basé sur une négociation stigmergique entre les agents. Le cas applicatif utilisé par l'approche CESNA et permettant d'illustrer ces travaux est le problème de partage de ressources critiques, caractérisé par un ensemble restreint de ressources exploitées par un grand nombre de consommateurs. Nos contributions sont de deux types : nous avons dans un premier temps proposé des évolutions de la représentation du problème exploitée par l'approche initiale (CESNA) afin d'en supprimer les limitations interdisant un passage à l'échelle, et dans un second temps, nous avons défini un nouveau modèle (MANA : Multi-level bAlancing Negotiating Agents) exploitant cette nouvelle représentation à l'aide d'un nouveau processus de résolution basé sur des mécanismes endogènes de contrôle de l'activité du système. Ces mécanismes reposent sur la matérialisation des effets microscopiques du phénomène macroscopique à orienter (le parcours de l'espace de recherche) afin de le rendre perceptible localement par les agents. Nos mesures montrent que ce nouveau modèle permet le passage à l'échelle (la résolution de problèmes industriels de grande taille) et une amélioration significative des performances de résolution par rapport à l'approche initiale montrant ainsi l'efficacité du guidage permis par les mécanismes utilisés. / This work addresses the issue of the endogenous control of Multi-Agents Systems (MAS) for solving complex problems, which we explore through the critical resources sharing problem. The complex problems we address are characterized by a combinatorial explosion of number of solutions with the size of the problems, a strong dynamic of the problem's data caused by an open environment in which many events can take place, a huge systemic complexity caused by the interdependencies between the many variables of the problem and a decentralization of the resolution process imposed by a physical and functional distribution of the variables incompatible with a centralized view of the problem. A complete course of the search space associated with such problems is unrealistic in an acceptable time, it is necessary to employ resolution methods known as incomplete. Whatever the incomplete approach considered, the incomplete course of the search space requires a control to maximize the probability of converging to a satisfactory solution. We identify three levels of control of the course of the search space regardless of the used approach : a static control (textit a priori definition of the behavior of the system), a dynamic control (evolving during the resolution according to pre-established mechanisms) and adaptive control (dynamically evolving during resolution). We show that an endogenous control of the system activity, ie. an adaptive control from the agents activity, is necessary to guide the course of the search space in the context of solving complex problems. This work was made in a context of industrial collaboration, they rely on an approach developed in previous work : CESNA (Complex Exchanges Between Stigmergic Negotiating Agents). CESNA is a multi-agent self-organizational approach using agents situated in an environment embodying the problem and used by a resolution process based on a stigmergic negotiation between agents. The application used by the CESNA approach allowing to illustrate this work is the critical resources sharing problem, characterized by a limited set of resources exploited by many consumers. Our contributions are of two kinds : we initially proposed changes in the representation of the problem used by the initial approach (CESNA) to remove restrictions prohibiting scalability, and in a second time we defined a new model (MANA : Multi-level balancing Negotiating Agents) using this new representation with a new resolution process based on endogenous control mechanisms of the system activity. These mechanisms are based on the materialization of the microscopic effects of the macroscopic phenomenon to direct (the path in the search space) to make it noticeable by agents. Our measurements show that this new model allows the scaling (the resolution of industrial problems) and a significant performances improvement of the resolution showing the effectiveness of the control allowed by the mechanisms used.