In a network, the components of a given system are represented as nodes, the interactions are abstracted as links between the nodes. Boolean networks refer to a class of dynamics on networks, in fact it is the simplest possible dynamics where each node has a value 0 or 1. This allows to investigate extensively the dynamics both analytically and by numerical experiments. The present article focuses on the theoretical concept of relevant components and their immediate application in plant biology. References for more in-depth treatment of the mathematical details are also given.

Orientador: Maria Beatriz Machado Bonacelli / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Geociências / Made available in DSpace on 2018-08-07T03:16:33Z (GMT). No. of bitstreams: 1 Dias_ElianeLaranja_M.pdf: 1534782 bytes, checksum: 198c16ef5c088bd64b1e73aa1de5da87 (MD5) Previous issue date: 2006 / Resumo: A revolução nas áreas científicas e tecnológicas nos últimos anos, assim como a complexidade que envolve, atualmente, o desenvolvimento da pesquisa vêm ensejando a organização de novos arranjos e formas de cooperação entre distintos atores que compõem o processo de inovação. As redes de pesquisa em biotecnologia e genômica, como programas de investigação e seus nexos com a geração de novas tecnologias, vêm se apresentando como exemplos recentes das novas formas de articulação, sendo um importante instrumento de desenvolvimento científico e tecnológico e de busca de maior competitividade. O objetivo dessa dissertação é o de analisar e avaliar as motivações para a formação de duas redes de pesquisa genômica no país: a do fungo Crinipellis perniciosa, causador da doença vassoura de bruxa em cacau e a do Genolyptus, que trabalha com o eucalipto. A primeira, com forte viés de política pública, procura enfrentar um dos principais problemas fitopatológicos do país - o ataque de fungos nos cacaueiros - e resgatar uma atividade de grande importância econômica e social, notadamente na Bahia. A segunda, formada por uma rede nacional de pesquisa pré-competitiva em eucalipto, visa aumentar a produtividade e a competitividade de um dos setores de destaque na geração de divisas para o Brasil - o de celulose e papel. Procura-se verificar a influência de diferentes elementos na sua estruturação, configuração e desenvolvimento, incluindo a dinâmica técnico-concorrencial dos mercados em questão (cacau e chocolate e papel e celulose), a organização da pesquisa e do processo inovativo e o papel estratégico dos diferentes atores envolvidos. O trabalho está estruturado em três capítulos, sendo que os dois primeiros discutem as principais ferramentas da abordagem evolucionista integrada aos conceitos de cadeias produtivas e inovativas e de redes de pesquisa. O terceiro capítulo discute a formação dessas duas redes genômicas, com base nos conceitos apresentados e em dados primários levantados, visando apontar os elementos mais relevantes para a compreensão da constituição e do desenvolvimento das atividades relacionadas às redes, assim como suas principais particularidades. A realização da dissertação permitiu identificar um referencial importante para o estudo de arranjos cooperativos que congrega elementos das pesquisas sobre redes e sobre cadeias produtivas e inovativas, aplicando-o ao estudo das redes genômicas em questão. A contribuição deste estudo reside na proposição de que a articulação de uma rede de pesquisa é fortemente influenciada pelas características das cadeias produtivas e inovativas dos setores industriais envolvidos / Abstract: In recent years, the revolutions in science and technology and the complexity inherent to research development have led to the organization of nove I cooperation arrangements between the different parties involved in the process of innovation. Research networks in the areas of biotechnology and genomics, as well as research programs aimed at the production of new technologies are recent examples of these novel associations, constituting an important element in scientific and technological development and in the search for higher levels of competitive ability. The goal of this dissertation was to analyze and evaluate the motivations for the creation of two genomic research networks in Brazil: the Crinipellis perniciosa Genome Project, a fungus that causes. Witches' Broom disease of cocoa, and the Genolyptus Project, aimed at deciphering the transcriptome of Eucalyptus crops. The first network has a strong orientation towards public policies and aims at solving one of the main phytopathological problems in the country - fungal diseases of cacao trees - and to restore an agricultural activity of great economical and social importance to the State of Bahia. The latter network consists of a nation-wide association of pre-competitive research in Eucalyptus that aims at increasing the productivity and the competitive ability of one of the main production sectors that contributes to the generation of monetary assets in Brazil: the business of pulp and paper. We intend to verify the influence of the different elements to network structure, configuration, and development, also including the technical-competitive dynamics of the markets under consideration (cocoa and chocolate, as well as pulp and paper), the organization ofthe research and innovation process, and the strategic role of the different parties involved. This work is organized into three chapters, with the first two discussing the main tools used for the evolutionary approach integrated to the concepts of productive and innovative chains and research networks. The third chapter discusses the creation of these two genomic networks, based on the concepts presented previously and on the primary data obtained, with the intention of pointing out the most important elements necessary to understand the constitution and development of the activities related to these networks, as well as their main particularities. The realization of this dissertation allowed the identification of an important referential for the study of cooperative arrangements that conjugates elements of the research concerning networks and productive and innovative chains, and finally applying this referential to the study of the genomic networks under consideration. The contribution of this study resides in the proposition that the organization of a research network is strongly influenced by the particular characteristics of the productive and innovative chains ofthe industrial sectors involved. / Mestrado / Mestre em Política Científica e Tecnológica

Le cerveau humain est un réseau «large-échelle» formé de régions corticales distribuées et fonctionnellement interconnectées. Le traitement de l'information par le cerveau est un processus dynamique mettant en jeu une réorganisation rapide des réseaux cérébraux fonctionnels, sur une échelle de temps très courte (inférieure à la seconde). Dans le champ des neurosciences cognitives, deux grandes questions restent ouvertes concernant ces réseaux. D'une part, est-il possible de suivre leur dynamique spatio-temporelle avec une résolution temporelle nettement supérieure à celle de l'IRM fonctionnelle? D'autre part, est-il possible de mettre en évidence des différences significatives dans ces réseaux lorsque le cerveau traite des stimuli (visuels, par exemple) ayant des caractéristiques différentes. Ces deux questions ont guidé les développements méthodologiques élaborés dans cette thèse. En effet, de nouvelles méthodes basées sur l'électroencéphalographie sont proposées. Ces méthodes permettent, d'une part de suivre la reconfiguration dynamique des réseaux cérébraux fonctionnels à une échelle de temps inférieure à la seconde. Elles permettent, d'autre part, de comparer deux réseaux cérébraux activés dans des conditions spécifiques. Nous proposons donc un nouvel algorithme bénéficiant de l'excellente résolution temporelle de l'EEG afin de suivre la reconfiguration rapide des réseaux fonctionnels cérébraux à l'échelle de la milliseconde. L'objectif principal de cet algorithme est de segmenter les réseaux cérébraux en un ensemble d' «états de connectivité fonctionnelle» à l'aide d'une approche de type « clustering ». L'algorithme est basé sur celui des K-means et a été appliqué sur les graphes de connectivité obtenus à partir de l'estimation des valeurs de connectivité fonctionnelle entre les régions d'intérêt considérées. La seconde question abordée dans ce travail relève de la mesure de similarité entre graphes. Ainsi, afin de comparer des réseaux de connectivité fonctionnelle, nous avons développé un algorithme (SimNet) capable de quantifier la similarité entre deux réseaux dont les nœuds sont définis spatialement. Cet algorithme met en correspondance les deux graphes en « déformant » le premier pour le rendre identique au second sur une contrainte de coût minimal associée à la déformation (insertion, suppression, substitution de nœuds et d’arêtes). Il procède selon deux étapes, la première consistant à calculer une distance sur les nœuds et la seconde une distance sur les arrêtes. Cet algorithme fournit un indice de similarité normalisé: 0 pour aucune similarité et 1 pour deux réseaux identiques. Il a été évalué sur des graphes simulés puis comparé à des algorithmes existants. Il montre de meilleures performances pour détecter la variation spatiale entre les graphes. Il a également été appliqué sur des données réelles afin de comparer différents réseaux cérébraux. Les résultats ont montré des performances élevées pour comparer deux réseaux cérébraux réels obtenus à partir l'EEG à haute résolution spatiale, au cours d'une tâche cognitive consistant à nommer des éléments de deux catégories différentes (objets vs animaux). / The human brain is a "large-scale" network consisting of distributed and functionally interconnected regions. The information processing in the brain is a dynamic process that involves a fast reorganization of functional brain networks in a very short time scale (less than one second). In the field of cognitive neuroscience, two big questions remain about these networks. Firstly, is it possible to follow the spatiotemporal dynamics of the brain networks with a temporal resolution significantly higher than the functional MRI? Secondly, is it possible to detect a significant difference between these networks when the brain processes stimuli (visual, for example) with different characteristics? These two questions are the main motivations of this thesis. Indeed, we proposed new methods based on dense electroencephalography. These methods allow: i) to follow the dynamic reconfiguration of brain functional networks at millisecond time scale and ii) to compare two activated brain networks under specific conditions. We propose a new algorithm benefiting from the excellent temporal resolution of EEG to track the fast reconfiguration of the functional brain networks at millisecond time scale. The main objective of this algorithm is to segment the brain networks into a set of "functional connectivity states" using a network-clustering approach. The algorithm is based on K-means and was applied on the connectivity graphs obtained by estimation the functional connectivity values between the considered regions of interest. The second challenge addressed in this work falls within the measure of similarity between graphs. Thus, to compare functional connectivity networks, we developed an algorithm (SimNet) that able to quantify the similarity between two networks whose node coordinates is known. This algorithm maps one graph to the other using different operations (insertion, deletion, substitution of nodes and edges). The algorithm is based on two main parts, the first one is based on calculating the nodes distance and the second one is to calculate the edges distance. This algorithm provides a normalized similarity index: 0 for no similarity and 1 for two identical networks. SimNet was evaluated with simulated graphs and was compared with previously-published graph similarity algorithms. It shows high performance to detect the similarity variation between graphs involving a shifting of the location of nodes. It was also applied on real data to compare different brain networks. Results showed high performance in the comparison of real brain networks obtained from dense EEG during a cognitive task consisting in naming items of two different categories (objects vs. animals).