Noms que formen part d'un determinant complex

Aquest article examina un aspecte de la informació gramatical que inclouen els diccionaris. En concret, analitza el tractament lexicogràfic que els noms que poden formar part d'un determinant complex han rebut en diversos diccionaris. Són noms que, segons els contextos funcionen com a nucli d'un sintagma nominal o com a nucli d'un sintagma determinant. Els resultats d'aquest estudi demostren que la informació gramatical en aquest tipus de noms en la majoria de diccionaris és molt pobre i fins i tot nul·la. Com a alternativa, el treball proposa un primer disseny d'entrada lexicogràfica prototípica per aquest tipus de noms que al costat de la informació semàntica té en compte la informació gramatical i la informació pragmàtica.

The SM protein Sly1 accelerates assembly of the ER-Golgi SNARE complex.

Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) and Sec1/Munc18 (SM) proteins constitute the core of an ancient vesicle fusion machine that diversified into distinct sets that now function in different trafficking steps in eukaryotic cells. Deciphering their precise mode of action has proved challenging. SM proteins are thought to act primarily through one type of SNARE protein, the syntaxins. Despite high structural similarity, however, contrasting binding modes have been found for different SM proteins and syntaxins. Whereas the secretory SM protein Munc18 binds to the ‟closed conformation" of syntaxin 1, the ER-Golgi SM protein Sly1 interacts only with the N-peptide of Sed5. Recent findings, however, indicate that SM proteins might interact simultaneously with both syntaxin regions. In search for a common mechanism, we now reinvestigated the Sly1/Sed5 interaction. We found that individual Sed5 adopts a tight closed conformation. Sly1 binds to both the closed conformation and the N-peptide of Sed5, suggesting that this is the original binding mode of SM proteins and syntaxins. In contrast to Munc18, however, Sly1 facilitates SNARE complex formation by loosening the closed conformation of Sed5.

Alpine tectonic evolution of a Jurassic subduction-accretionary complex: Deformation, kinematics and 40Ar/39Ar age constraints on the Mesozoic low-grade schists of the Circum-Rhodope Belt in the eastern Rhodope-Thrace region, Bulgaria-Greece

Deformation of the Circum-Rhodope Belt Mesozoic (Middle Triassic to earliest Lower Cretaceous) low-grade schists underneath an arc-related ophiolitic magmatic suite and associated sedimentary successions in the eastern Rhodope-Thrace region occurred as a two-episode tectonic process: (i) Late Jurassic deformation of arc to margin units resulting from the eastern Rhodope-Evros arc-Rhodope terrane continental margin collision and accretion to that margin, and (ii) Middle Eocene deformation related to the Tertiary crustal extension and final collision resulting in the closure of the Vardar ocean south of the Rhodope terrane. The first deformational event D-1 is expressed by Late Jurassic NW-N vergent fold generations and the main and subsidiary planar-linear structures. Although overprinting, these structural elements depict uniform bulk north-directed thrust kinematics and are geometrically compatible with the increments of progressive deformation that develops in same greenschist-facies metamorphic grade. It followed the Early-Middle Jurassic magmatic evolution of the eastern Rhodope-Evros arc established on the upper plate of the southward subducting Maliac-Meliata oceanic lithosphere that established the Vardar Ocean in a supra-subduction back-arc setting. This first event resulted in the thrust-related tectonic emplacement of the Mesozoic schists in a supra-crustal level onto the Rhodope continental margin. This Late Jurassic-Early Cretaceous tectonic event related to N-vergent Balkan orogeny is well-constrained by geochronological data and traced at a regional-scale within distinct units of the Carpatho-Balkan Belt. Following subduction reversal towards the north whereby the Vardar Ocean was subducted beneath the Rhodope margin by latest Cretaceous times, the low-grade schists aquired a new position in the upper plate, and hence, the Mesozoic schists are lacking the Cretaceous S-directed tectono-metamorphic episode whose effects are widespread in the underlying high-grade basement. The subduction of the remnant Vardar Ocean located behind the colliding arc since the middle Cretaceous was responsible for its ultimate closure, Early Tertiary collision with the Pelagonian block and extension in the region caused the extensional collapse related to the second deformational event D-2. This extensional episode was experienced passively by the Mesozoic schists located in the hanging wall of the extensional detachments in Eocene times. It resulted in NE-SW oriented open folds representing corrugation antiforms of the extensional detachment surfaces, brittle faulting and burial history beneath thick Eocene sediments as indicated by 42.1-39.7 Ma Ar-40/Ar-39 mica plateau ages obtained in the study. The results provide structural constraints for the involvement components of Jurassic paleo-subduction zone in a Late Jurassic arc-continental margin collisional history that contributed to accretion-related crustal growth of the Rhodope terrane. (C) 2011 Elsevier Ltd. All rights reserved.

The challenge of modeling nuclear receptor regulatory networks in mammalian cells.

Nuclear receptors are a major component of signal transduction in animals. They mediate the regulatory activities of many hormones, nutrients and metabolites on the homeostasis and physiology of cells and tissues. It is of high interest to model the corresponding regulatory networks. While molecular and cell biology studies of individual promoters have provided important mechanistic insight, a more complex picture is emerging from genome-wide studies. The regulatory circuitry of nuclear receptor regulated gene expression networks, and their response to cellular signaling, appear highly dynamic, and involve long as well as short range chromatin interactions. We review how progress in understanding the kinetics and regulation of cofactor recruitment, and the development of new genomic methods, provide opportunities but also a major challenge for modeling nuclear receptor mediated regulatory networks.

Relatedness influences signal reliability in evolving robots.

Communication is an indispensable component of animal societies, yet many open questions remain regarding the factors affecting the evolution and reliability of signalling systems. A potentially important factor is the level of genetic relatedness between signallers and receivers. To quantitatively explore the role of relatedness in the evolution of reliable signals, we conducted artificial evolution over 500 generations in a system of foraging robots that can emit and perceive light signals. By devising a quantitative measure of signal reliability, and comparing independently evolving populations differing in within-group relatedness, we show a strong positive correlation between relatedness and reliability. Unrelated robots produced unreliable signals, whereas highly related robots produced signals that reliably indicated the location of the food source and thereby increased performance. Comparisons across populations also revealed that the frequency for signal production-which is often used as a proxy of signal reliability in empirical studies on animal communication-is a poor predictor of signal reliability and, accordingly, is not consistently correlated with group performance. This has important implications for our understanding of signal evolution and the empirical tools that are used to investigate communication.

Source wavelet estimation for waveform inversion of crosshole georadar data

AbstractFor a wide range of environmental, hydrological, and engineering applications there is a fast growing need for high-resolution imaging. In this context, waveform tomographic imaging of crosshole georadar data is a powerful method able to provide images of pertinent electrical properties in near-surface environments with unprecedented spatial resolution. In contrast, conventional ray-based tomographic methods, which consider only a very limited part of the recorded signal (first-arrival traveltimes and maximum first-cycle amplitudes), suffer from inherent limitations in resolution and may prove to be inadequate in complex environments. For a typical crosshole georadar survey the potential improvement in resolution when using waveform-based approaches instead of ray-based approaches is in the range of one order-of- magnitude. Moreover, the spatial resolution of waveform-based inversions is comparable to that of common logging methods. While in exploration seismology waveform tomographic imaging has become well established over the past two decades, it is comparably still underdeveloped in the georadar domain despite corresponding needs. Recently, different groups have presented finite-difference time-domain waveform inversion schemes for crosshole georadar data, which are adaptations and extensions of Tarantola's seminal nonlinear generalized least-squares approach developed for the seismic case. First applications of these new crosshole georadar waveform inversion schemes on synthetic and field data have shown promising results. However, there is little known about the limits and performance of such schemes in complex environments. To this end, the general motivation of my thesis is the evaluation of the robustness and limitations of waveform inversion algorithms for crosshole georadar data in order to apply such schemes to a wide range of real world problems.One crucial issue to making applicable and effective any waveform scheme to real-world crosshole georadar problems is the accurate estimation of the source wavelet, which is unknown in reality. Waveform inversion schemes for crosshole georadar data require forward simulations of the wavefield in order to iteratively solve the inverse problem. Therefore, accurate knowledge of the source wavelet is critically important for successful application of such schemes. Relatively small differences in the estimated source wavelet shape can lead to large differences in the resulting tomograms. In the first part of my thesis, I explore the viability and robustness of a relatively simple iterative deconvolution technique that incorporates the estimation of the source wavelet into the waveform inversion procedure rather than adding additional model parameters into the inversion problem. Extensive tests indicate that this source wavelet estimation technique is simple yet effective, and is able to provide remarkably accurate and robust estimates of the source wavelet in the presence of strong heterogeneity in both the dielectric permittivity and electrical conductivity as well as significant ambient noise in the recorded data. Furthermore, our tests also indicate that the approach is insensitive to the phase characteristics of the starting wavelet, which is not the case when directly incorporating the wavelet estimation into the inverse problem.Another critical issue with crosshole georadar waveform inversion schemes which clearly needs to be investigated is the consequence of the common assumption of frequency- independent electromagnetic constitutive parameters. This is crucial since in reality, these parameters are known to be frequency-dependent and complex and thus recorded georadar data may show significant dispersive behaviour. In particular, in the presence of water, there is a wide body of evidence showing that the dielectric permittivity can be significantly frequency dependent over the GPR frequency range, due to a variety of relaxation processes. The second part of my thesis is therefore dedicated to the evaluation of the reconstruction limits of a non-dispersive crosshole georadar waveform inversion scheme in the presence of varying degrees of dielectric dispersion. I show that the inversion algorithm, combined with the iterative deconvolution-based source wavelet estimation procedure that is partially able to account for the frequency-dependent effects through an "effective" wavelet, performs remarkably well in weakly to moderately dispersive environments and has the ability to provide adequate tomographic reconstructions.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

A coiled coil trigger site is essential for rapid binding of synaptobrevin to the SNARE acceptor complex.

Exocytosis from synaptic vesicles is driven by stepwise formation of a tight alpha-helical complex between the fusing membranes. The complex is composed of the three SNAREs: synaptobrevin 2, SNAP-25, and syntaxin 1a. An important step in complex formation is fast binding of vesicular synaptobrevin to the preformed syntaxin 1.SNAP-25 dimer. Exactly how this step relates to neurotransmitter release is not well understood. Here, we combined different approaches to gain insights into this reaction. Using computational methods, we identified a stretch in synaptobrevin 2 that may function as a coiled coil "trigger site." This site is also present in many synaptobrevin homologs functioning in other trafficking steps. Point mutations in this stretch inhibited binding to the syntaxin 1.SNAP-25 dimer and slowed fusion of liposomes. Moreover, the point mutations severely inhibited secretion from chromaffin cells. Altogether, this demonstrates that the trigger site in synaptobrevin is crucial for productive SNARE zippering.

Study of the proteolytic activity of the paracaspase MALT1 in T cell activation

Summary : Several signalling cascades are initiated through the triggering of the T cell receptor (TCR) by an antigenic peptide expressed at the surface of an antigen presenting cell. These pathways lead to morphological changes controlling T cell adhesiveness and migration to the site of infection, and to the activation of transcription factors that regulate key genes for the proper development of the immune response. Amongst them, the nuclear factor xB (NF-κB) is the subject of intense research since more than twenty years because deregulated NF-κB signalling in lymphocytes can lead to immunodeficiency, autoimmunity or lymphomas. Therefore, the understanding of the molecular mechanisms regulating NF-κB activation is important for the development of new therapeutics aimed at treating various diseases. In T lymphocytes, a complex composed of CARMAI, BCL10 and MALT1 relays signals from TCR proximal events to NF-κB activation. Gene translocations of the BCL10 or MALTI genes or oncogenic mutations affecting CARNA 1 result in constitutive NF-κB activation and are related to the development of certain forms of lymphomas. MALT1 contains acaspase-like domain, but it is unknown whether this domain is proteolytically active. In this study, we found that MALT1 has arginine-directed proteolytic activity. We showed that the proteolytic activity of MALT 1 is key to TCR-induced NF-κB activation and production of interleukin 2. We identified BCL 10 as a MALT 1 substrate, and we showed that its cleavage regulates T cell adhesion to the extracellular matrix protein fibronectin. Furthermore, we identified caspase 10 as another substrate of MALT1. caspase 10 is a close homologue of caspase 8 and is known to be involved in the induction of apoptosis upon Fast or TRAIL stimulation. We showed that caspase 10 is important for TCR-induced NF-κB activation and interleukin 2 production, identifying for the first time a non apoptotic function for caspase 10. These data provide evidence for previously uncharacterized roles of MALT 1 and BCL 10 in the regulation of T cell adhesion and of caspase 10 in the activation of lymphocytes, and allow a better understanding of the molecular mechanisms of T lymphocyte activation. Since the proteolytic activity of MALT1 is essential to T cell activation, it suggests that the targeting of this activity may be relevant for the development of immunomodulatory or anticancer drugs. Résumé : De nombreuses voies de signalisation sont initiées via la stimulation des récepteurs des cellules T (TCR) par un peptide antigénique exprimé à la surface d'une cellule présentatrice d'antigènes. Ces cascades de signalisation produisent des changements morphologiques qui contrôlent l'adhésion des cellules T et leur migration vers le site d'infection. Elles contrôlent également l'activation de facteurs de transcription qui régulent la transcription de gènes importants pour la réponse immunitaire. Parmi ces derniers, le facteur nucléaire KB (NF-κB) joue un rôle essentiel, puisqu'une régulation aberrante de son activité dans les lymphocytes peut causer des immunodéficiences, des maladies autoimmunes ou des lymphomes. C'est pour cela que la compréhension des mécanismes moléculaires qui contrôlent l'activation de NF-κB est donc importante pour le développement de nouvelles thérapies. Un complexe contenant les protéines CAIZMAI, BCL10 et MALT1 transmet, dans les lymphocytes T, le signal du TCR vers l'activation de NF-κB. Des translocations des gènes qui codent pour BCL10 et MALTI et des mutations affectant la fonction de CARNAI ont été liées au développement de certaines formes de lymphomes. MALTI contient un domaine qui ressemble au domaine catalytique présent dans les caspases, mais il n'est pas connu si ce domaine a une activité protéolytique. Dans cette étude, nous avons découvert que MALTI est une protéase qui a une spécificité pour les acides aminés basiques comme l'arginine. Nous montrons que l'activité protéolytique de MALTI est importante pour l'activation de NF-κB et la production d'interleukine 2 après stimulation du TCR. Nous avons observé que BCL10 est clivé par MALTI pendant l'activation des lymphocytes T, et que ce clivage est impliqué dans la régulation de l'adhésion des lymphocytes T à la fibronectin, une protéine de la matrice extracellulaire. De plus, nous avons identifié que la caspase 10, qui a une grande homologie avec la caspase 8 et qui jusqu'à maintenant est connue pour son rôle dans l'induction de la mort cellulaire en réponse à une stimulation par Fast ou par TRAIL, est également un substrat de MALT 1. En montrant que la caspase 10 est nécessaire à l' activation de NF-icB et à la production de l'interleukine 2 après stimulation du TCR, nous décrivons pour la première fois une fonction non apoptotique de la caspase 10. Ces résultats décrivent de nouveaux rôles pour MALT1 et BCL10 dans le contrôle de l'adhésion des lymphocytes T et de la caspase 10 pour l'activation des lymphocytes T. Puisque l'activité protéolytique de MALT1 est essentielle pour l'activation des lymphocytes T, nous suggérons que cibler cette activité protéolytique de MALT 1 pourrait amener de nouvelles possibilités de traitement de maladies où une activation aberrante des lymphocytes est impliquée.

Pivotal role for a tail subunit of the RNA polymerase II mediator complex CgMed2 in azole tolerance and adherence in Candida glabrata.

Antifungal therapy failure can be associated with increased resistance to the employed antifungal agents. Candida glabrata, the second most common cause of invasive candidiasis, is intrinsically less susceptible to the azole class of antifungals and accounts for 15% of all Candida bloodstream infections. Here, we show that C. glabrata MED2 (CgMED2), which codes for a tail subunit of the RNA polymerase II Mediator complex, is required for resistance to azole antifungal drugs in C. glabrata. An inability to transcriptionally activate genes encoding a zinc finger transcriptional factor, CgPdr1, and multidrug efflux pump, CgCdr1, primarily contributes to the elevated susceptibility of the Cgmed2Δ mutant toward azole antifungals. We also report for the first time that the Cgmed2Δ mutant exhibits sensitivity to caspofungin, a constitutively activated protein kinase C-mediated cell wall integrity pathway, and elevated adherence to epithelial cells. The increased adherence of the Cgmed2Δ mutant was attributed to the elevated expression of the EPA1 and EPA7 genes. Further, our data demonstrate that CgMED2 is required for intracellular proliferation in human macrophages and modulates survival in a murine model of disseminated candidiasis. Lastly, we show an essential requirement for CgMed2, along with the Mediator middle subunit CgNut1 and the Mediator cyclin-dependent kinase/cyclin subunit CgSrb8, for the high-level fluconazole resistance conferred by the hyperactive allele of CgPdr1. Together, our findings underscore a pivotal role for CgMed2 in basal tolerance and acquired resistance to azole antifungals.

Error and attack tolerance of layered complex networks.

Many complex systems may be described by not one but a number of complex networks mapped on each other in a multi-layer structure. Because of the interactions and dependencies between these layers, the state of a single layer does not necessarily reflect well the state of the entire system. In this paper we study the robustness of five examples of two-layer complex systems: three real-life data sets in the fields of communication (the Internet), transportation (the European railway system), and biology (the human brain), and two models based on random graphs. In order to cover the whole range of features specific to these systems, we focus on two extreme policies of system's response to failures, no rerouting and full rerouting. Our main finding is that multi-layer systems are much more vulnerable to errors and intentional attacks than they appear from a single layer perspective.

Dissection of the complex phenotype in cuticular mutants of Arabidopsis reveals a role of SERRATE as a mediator.

Mutations in LACERATA (LCR), FIDDLEHEAD (FDH), and BODYGUARD (BDG) cause a complex developmental syndrome that is consistent with an important role for these Arabidopsis genes in cuticle biogenesis. The genesis of their pleiotropic phenotypes is, however, poorly understood. We provide evidence that neither distorted depositions of cutin, nor deficiencies in the chemical composition of cuticular lipids, account for these features, instead suggesting that the mutants alleviate the functional disorder of the cuticle by reinforcing their defenses. To better understand how plants adapt to these mutations, we performed a genome-wide gene expression analysis. We found that apparent compensatory transcriptional responses in these mutants involve the induction of wax, cutin, cell wall, and defense genes. To gain greater insight into the mechanism by which cuticular mutations trigger this response in the plants, we performed an overlap meta-analysis, which is termed MASTA (MicroArray overlap Search Tool and Analysis), of differentially expressed genes. This suggested that different cell integrity pathways are recruited in cesA cellulose synthase and cuticular mutants. Using MASTA for an in silico suppressor/enhancer screen, we identified SERRATE (SE), which encodes a protein of RNA-processing multi-protein complexes, as a likely enhancer. In confirmation of this notion, the se lcr and se bdg double mutants eradicate severe leaf deformations as well as the organ fusions that are typical of lcr and bdg and other cuticular mutants. Also, lcr does not confer resistance to Botrytis cinerea in a se mutant background. We propose that there is a role for SERRATE-mediated RNA signaling in the cuticle integrity pathway.

Evolutionary games on complex networks : the emergence and maintenance of cooperation

Abstract The object of game theory lies in the analysis of situations where different social actors have conflicting requirements and where their individual decisions will all influence the global outcome. In this framework, several games have been invented to capture the essence of various dilemmas encountered in many common important socio-economic situations. Even though these games often succeed in helping us understand human or animal behavior in interactive settings, some experiments have shown that people tend to cooperate with each other in situations for which classical game theory strongly recommends them to do the exact opposite. Several mechanisms have been invoked to try to explain the emergence of this unexpected cooperative attitude. Among them, repeated interaction, reputation, and belonging to a recognizable group have often been mentioned. However, the work of Nowak and May (1992) showed that the simple fact of arranging the players according to a spatial structure and only allowing them to interact with their immediate neighbors is sufficient to sustain a certain amount of cooperation even when the game is played anonymously and without repetition. Nowak and May's study and much of the following work was based on regular structures such as two-dimensional grids. Axelrod et al. (2002) showed that by randomizing the choice of neighbors, i.e. by actually giving up a strictly local geographical structure, cooperation can still emerge, provided that the interaction patterns remain stable in time. This is a first step towards a social network structure. However, following pioneering work by sociologists in the sixties such as that of Milgram (1967), in the last few years it has become apparent that many social and biological interaction networks, and even some technological networks, have particular, and partly unexpected, properties that set them apart from regular or random graphs. Among other things, they usually display broad degree distributions, and show small-world topological structure. Roughly speaking, a small-world graph is a network where any individual is relatively close, in terms of social ties, to any other individual, a property also found in random graphs but not in regular lattices. However, in contrast with random graphs, small-world networks also have a certain amount of local structure, as measured, for instance, by a quantity called the clustering coefficient. In the same vein, many real conflicting situations in economy and sociology are not well described neither by a fixed geographical position of the individuals in a regular lattice, nor by a random graph. Furthermore, it is a known fact that network structure can highly influence dynamical phenomena such as the way diseases spread across a population and ideas or information get transmitted. Therefore, in the last decade, research attention has naturally shifted from random and regular graphs towards better models of social interaction structures. The primary goal of this work is to discover whether or not the underlying graph structure of real social networks could give explanations as to why one finds higher levels of cooperation in populations of human beings or animals than what is prescribed by classical game theory. To meet this objective, I start by thoroughly studying a real scientific coauthorship network and showing how it differs from biological or technological networks using divers statistical measurements. Furthermore, I extract and describe its community structure taking into account the intensity of a collaboration. Finally, I investigate the temporal evolution of the network, from its inception to its state at the time of the study in 2006, suggesting also an effective view of it as opposed to a historical one. Thereafter, I combine evolutionary game theory with several network models along with the studied coauthorship network in order to highlight which specific network properties foster cooperation and shed some light on the various mechanisms responsible for the maintenance of this same cooperation. I point out the fact that, to resist defection, cooperators take advantage, whenever possible, of the degree-heterogeneity of social networks and their underlying community structure. Finally, I show that cooperation level and stability depend not only on the game played, but also on the evolutionary dynamic rules used and the individual payoff calculations. Synopsis Le but de la théorie des jeux réside dans l'analyse de situations dans lesquelles différents acteurs sociaux, avec des objectifs souvent conflictuels, doivent individuellement prendre des décisions qui influenceront toutes le résultat global. Dans ce cadre, plusieurs jeux ont été inventés afin de saisir l'essence de divers dilemmes rencontrés dans d'importantes situations socio-économiques. Bien que ces jeux nous permettent souvent de comprendre le comportement d'êtres humains ou d'animaux en interactions, des expériences ont montré que les individus ont parfois tendance à coopérer dans des situations pour lesquelles la théorie classique des jeux prescrit de faire le contraire. Plusieurs mécanismes ont été invoqués pour tenter d'expliquer l'émergence de ce comportement coopératif inattendu. Parmi ceux-ci, la répétition des interactions, la réputation ou encore l'appartenance à des groupes reconnaissables ont souvent été mentionnés. Toutefois, les travaux de Nowak et May (1992) ont montré que le simple fait de disposer les joueurs selon une structure spatiale en leur permettant d'interagir uniquement avec leurs voisins directs est suffisant pour maintenir un certain niveau de coopération même si le jeu est joué de manière anonyme et sans répétitions. L'étude de Nowak et May, ainsi qu'un nombre substantiel de travaux qui ont suivi, étaient basés sur des structures régulières telles que des grilles à deux dimensions. Axelrod et al. (2002) ont montré qu'en randomisant le choix des voisins, i.e. en abandonnant une localisation géographique stricte, la coopération peut malgré tout émerger, pour autant que les schémas d'interactions restent stables au cours du temps. Ceci est un premier pas en direction d'une structure de réseau social. Toutefois, suite aux travaux précurseurs de sociologues des années soixante, tels que ceux de Milgram (1967), il est devenu clair ces dernières années qu'une grande partie des réseaux d'interactions sociaux et biologiques, et même quelques réseaux technologiques, possèdent des propriétés particulières, et partiellement inattendues, qui les distinguent de graphes réguliers ou aléatoires. Entre autres, ils affichent en général une distribution du degré relativement large ainsi qu'une structure de "petit-monde". Grossièrement parlant, un graphe "petit-monde" est un réseau où tout individu se trouve relativement près de tout autre individu en termes de distance sociale, une propriété également présente dans les graphes aléatoires mais absente des grilles régulières. Par contre, les réseaux "petit-monde" ont, contrairement aux graphes aléatoires, une certaine structure de localité, mesurée par exemple par une quantité appelée le "coefficient de clustering". Dans le même esprit, plusieurs situations réelles de conflit en économie et sociologie ne sont pas bien décrites ni par des positions géographiquement fixes des individus en grilles régulières, ni par des graphes aléatoires. De plus, il est bien connu que la structure même d'un réseau peut passablement influencer des phénomènes dynamiques tels que la manière qu'a une maladie de se répandre à travers une population, ou encore la façon dont des idées ou une information s'y propagent. Ainsi, durant cette dernière décennie, l'attention de la recherche s'est tout naturellement déplacée des graphes aléatoires et réguliers vers de meilleurs modèles de structure d'interactions sociales. L'objectif principal de ce travail est de découvrir si la structure sous-jacente de graphe de vrais réseaux sociaux peut fournir des explications quant aux raisons pour lesquelles on trouve, chez certains groupes d'êtres humains ou d'animaux, des niveaux de coopération supérieurs à ce qui est prescrit par la théorie classique des jeux. Dans l'optique d'atteindre ce but, je commence par étudier un véritable réseau de collaborations scientifiques et, en utilisant diverses mesures statistiques, je mets en évidence la manière dont il diffère de réseaux biologiques ou technologiques. De plus, j'extrais et je décris sa structure de communautés en tenant compte de l'intensité d'une collaboration. Finalement, j'examine l'évolution temporelle du réseau depuis son origine jusqu'à son état en 2006, date à laquelle l'étude a été effectuée, en suggérant également une vue effective du réseau par opposition à une vue historique. Par la suite, je combine la théorie évolutionnaire des jeux avec des réseaux comprenant plusieurs modèles et le réseau de collaboration susmentionné, afin de déterminer les propriétés structurelles utiles à la promotion de la coopération et les mécanismes responsables du maintien de celle-ci. Je mets en évidence le fait que, pour ne pas succomber à la défection, les coopérateurs exploitent dans la mesure du possible l'hétérogénéité des réseaux sociaux en termes de degré ainsi que la structure de communautés sous-jacente de ces mêmes réseaux. Finalement, je montre que le niveau de coopération et sa stabilité dépendent non seulement du jeu joué, mais aussi des règles de la dynamique évolutionnaire utilisées et du calcul du bénéfice d'un individu.

Effects of phylogenetic signal on ancestral state reconstruction.

One of the standard tools used to understand the processes shaping trait evolution along the branches of a phylogenetic tree is the reconstruction of ancestral states (Pagel 1999). The purpose is to estimate the values of the trait of interest for every internal node of a phylogenetic tree based on the trait values of the extant species, a topology and, depending on the method used, branch lengths and a model of trait evolution (Ronquist 2004). This approach has been used in a variety of contexts such as biogeography (e.g., Nepokroeff et al. 2003, Blackburn 2008), ecological niche evolution (e.g., Smith and Beaulieu 2009, Evans et al. 2009) and metabolic pathway evolution (e.g., Gabaldón 2003, Christin et al. 2008). Investigations of the factors affecting the accuracy with which ancestral character states can be reconstructed have focused in particular on the choice of statistical framework (Ekman et al. 2008) and the selection of the best model of evolution (Cunningham et al. 1998, Mooers et al. 1999). However, other potential biases affecting these methods, such as the effect of tree shape (Mooers 2004), taxon sampling (Salisbury and Kim 2001) as well as reconstructing traits involved in species diversification (Goldberg and Igić 2008), have also received specific attention. Most of these studies conclude that ancestral character states reconstruction is still not perfect, and that further developments are necessary to improve its accuracy (e.g., Christin et al. 2010). Here, we examine how different estimations of branch lengths affect the accuracy of ancestral character state reconstruction. In particular, we tested the effect of using time-calibrated versus molecular branch lengths and provide guidelines to select the most appropriate branch lengths to reconstruct the ancestral state of a trait.