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High-throughput technology is changing the face of research biology, generating an ever growing amount of large-scale data sets. With experiments utilizing next-generation gene sequencing, mass spectrometry, and various other global surveys of proteins, the task of translating the plethora of data into biology has become a daunting task. In response, functional networks have been developed as a means for integrating the data into models of proteomic organization. In these networks, proteins are linked if they are evidenced to operate together in the same function, facilitating predictions about the functions, phenotypes, and disease associations of uncharacterized genes. In this body of work, we explore different applications of this so-called "guilt-by-association" concept to predict loss-of-function phenotypes and diseases associated with genes in yeast, worm, and human. We also scrutinize certain limitations associated with the functional networks, predictive methods, and measures of performance used in our studies. Importantly, the predictive method and performance measure, if not chosen appropriately for the biological objective at hand, can largely distort the results and interpretation of a study. These findings are incorporated in the development of RIDDLE, a method for characterizing whole sets of genes. This machine learning-based method provides a measure of network distance, and thus functional association, between two sets of genes. RIDDLE may be applied to a wide range of potential applications, as we demonstrate with several biological examples, including linking microRNA-450a to ocular development and disease. In the last decade, functional networks have proven to be a useful strategy for interpreting large-scale proteomic and genomic data sets. With the continued growth of genome coverage in networks and the innovation of predictive methods, we will surely advance towards our ultimate goal of understanding the genetic changes that underlie disease. / text

Background: The management accounting coordination in a horizontal network of small and medium sized companies entails numerous questions and potential complications that may vary according to the extent of cooperation chosen. Categorizing network co-operations along an imaginary market–hierarchy continuum might offer the theoretical foundation needed to further understand networks and as a consequence illuminate the link to the diverse management accounting issues. Purpose: To analyze the management accounting systems in different forms of horizontal network co-operations. Delimitations: It is not within the purpose of the thesis to discuss the various reasons why cooperative relationships are established in the first place, nor how they develop. Proceed of the study: A multiple case study of three Swedish networks composed of small/medium-sized firms was carried out with the help of thirteen interviews. Conclusions: Empirical support is offered for both the business and functional network types. The model of Dahlgren et al. does not take into account that the degree of integration of the network supporting activities does not necessarily have to entail the equivalent degree of integration regarding the management accounting system. Hence, the typology of networks cannot be said to be conveniently applicable to study all horizontal networks, as the model may leave out certain significant dimensions that obstruct the understanding of a network.

<p>Background: The management accounting coordination in a horizontal network of small and medium sized companies entails numerous questions and potential complications that may vary according to the extent of cooperation chosen. Categorizing network co-operations along an imaginary market–hierarchy continuum might offer the theoretical foundation needed to further understand networks and as a consequence illuminate the link to the diverse management accounting issues. </p><p>Purpose: To analyze the management accounting systems in different forms of horizontal network co-operations. </p><p>Delimitations: It is not within the purpose of the thesis to discuss the various reasons why cooperative relationships are established in the first place, nor how they develop. Proceed of the study: A multiple case study of three Swedish networks composed of small/medium-sized firms was carried out with the help of thirteen interviews. </p><p>Conclusions: Empirical support is offered for both the business and functional network types. The model of Dahlgren et al. does not take into account that the degree of integration of the network supporting activities does not necessarily have to entail the equivalent degree of integration regarding the management accounting system. Hence, the typology of networks cannot be said to be conveniently applicable to study all horizontal networks, as the model may leave out certain significant dimensions that obstruct the understanding of a network.</p>

Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the Western world. It shows a high degree of genetic and phenotypic complexity with many implicated factors, various disease manifestations but few clear causal links. Ongoing research has identified a growing number of molecular alterations linked to the disease. Dopaminergic neurons in the substantia nigra, specifically their synapses, are the key-affected region in PD. Therefore, this work focuses on understanding the disease effects on the synapse, aiming to identify potential genetic triggers and synaptic PD associated mechanisms. Currently, one of the main challenges in this area is data quality and accessibility. In order to study PD, publicly available data were systematically retrieved and analysed. 418 PD associated genes could be identified, based on mutations and curated annotations. I curated an up-to-date and complete synaptic proteome map containing a total of 6,706 proteins. Region specific datasets describing the presynapse, postsynapse and synaptosome were also delimited. These datasets were analysed, investigating similarities and differences, including reproducibility and functional interpretations. The use of Protein-Protein-Interaction Network (PPIN) analysis was chosen to gain deeper knowledge regarding specific effects of PD on the synapse. Thus I generated a customised, filtered, human specific Protein-Protein Interaction (PPI) dataset, containing 211,824 direct interactions, from four public databases. Proteomics data and PPI information allowed the construction of PPINs. These were analysed and a set of low level statistics, including modularity, clustering coefficient and node degree, explaining the network’s topology from a mathematical point of view were obtained. Apart from low-level network statistics, high-level topology of the PPINs was studied. To identify functional network subgroups, different clustering algorithms were investigated. In the context of biological networks, the underlying hypothesis is that proteins in a structural community are more likely to share common functions. Therefore I attempted to identify PD enriched communities of synaptic proteins. Once identified, they were compared amongst each other. Three community clusters could be identified as containing largely overlapping gene sets. These contain 24 PD associated genes. Apart from the known disease associated genes in these communities, a total of 322 genes was identified. Each of the three clusters is specifically enriched for specific biological processes and cellular components, which include neurotransmitter secretion, positive regulation of synapse assembly, pre- and post-synaptic membrane, scaffolding proteins, neuromuscular junction development and complement activation (classical pathway) amongst others. The presented approach combined a curated set of PD associated genes, filtered PPI information and synaptic proteomes. Various small- and large-scale analytical approaches, including PPIN topology analysis, clustering algorithms and enrichment studies identified highly PD affected synaptic proteins and subregions. Specific disease associated functions confirmed known research insights and allowed me to propose a new list of so far unknown potential disease associated genes. Due to the open design, this approach can be used to answer similar research questions regarding other complex diseases amongst others.

Contexte La connectomique, ou la cartographie des connexions neuronales, est un champ de recherche des neurosciences évoluant rapidement, promettant des avancées majeures en ce qui concerne la compréhension du fonctionnement cérébral. La formation de circuits neuronaux en réponse à des stimuli environnementaux est une propriété émergente du cerveau. Cependant, la connaissance que nous avons de la nature précise de ces réseaux est encore limitée. Au niveau du cortex visuel, qui est l’aire cérébrale la plus étudiée, la manière dont les informations se transmettent de neurone en neurone est une question qui reste encore inexplorée. Cela nous invite à étudier l’émergence des microcircuits en réponse aux stimuli visuels. Autrement dit, comment l’interaction entre un stimulus et une assemblée cellulaire est-elle mise en place et modulée? Méthodes En réponse à la présentation de grilles sinusoïdales en mouvement, des ensembles neuronaux ont été enregistrés dans la couche II/III (aire 17) du cortex visuel primaire de chats anesthésiés, à l’aide de multi-électrodes en tungstène. Des corrélations croisées ont été effectuées entre l’activité de chacun des neurones enregistrés simultanément pour mettre en évidence les liens fonctionnels de quasi-synchronie (fenêtre de ± 5 ms sur les corrélogrammes croisés corrigés). Ces liens fonctionnels dévoilés indiquent des connexions synaptiques putatives entre les neurones. Par la suite, les histogrammes peri-stimulus (PSTH) des neurones ont été comparés afin de mettre en évidence la collaboration synergique temporelle dans les réseaux fonctionnels révélés. Enfin, des spectrogrammes dépendants du taux de décharges entre neurones ou stimulus-dépendants ont été calculés pour observer les oscillations gamma dans les microcircuits émergents. Un indice de corrélation (Rsc) a également été calculé pour les neurones connectés et non connectés. Résultats Les neurones liés fonctionnellement ont une activité accrue durant une période de 50 ms contrairement aux neurones fonctionnellement non connectés. Cela suggère que les connexions entre neurones mènent à une synergie de leur inter-excitabilité. En outre, l’analyse du spectrogramme dépendant du taux de décharge entre neurones révèle que les neurones connectés ont une plus forte activité gamma que les neurones non connectés durant une fenêtre d’opportunité de 50ms. L’activité gamma de basse-fréquence (20-40 Hz) a été associée aux neurones à décharge régulière (RS) et l’activité de haute fréquence (60-80 Hz) aux neurones à décharge rapide (FS). Aussi, les neurones fonctionnellement connectés ont systématiquement un Rsc plus élevé que les neurones non connectés. Finalement, l’analyse des corrélogrammes croisés révèle que dans une assemblée neuronale, le réseau fonctionnel change selon l’orientation de la grille. Nous démontrons ainsi que l’intensité des relations fonctionnelles dépend de l’orientation de la grille sinusoïdale. Cette relation nous a amené à proposer l’hypothèse suivante : outre la sélectivité des neurones aux caractères spécifiques du stimulus, il y a aussi une sélectivité du connectome. En bref, les réseaux fonctionnels «signature » sont activés dans une assemblée qui est strictement associée à l’orientation présentée et plus généralement aux propriétés des stimuli. Conclusion Cette étude souligne le fait que l’assemblée cellulaire, plutôt que le neurone, est l'unité fonctionnelle fondamentale du cerveau. Cela dilue l'importance du travail isolé de chaque neurone, c’est à dire le paradigme classique du taux de décharge qui a été traditionnellement utilisé pour étudier l'encodage des stimuli. Cette étude contribue aussi à faire avancer le débat sur les oscillations gamma, en ce qu'elles surviennent systématiquement entre neurones connectés dans les assemblées, en conséquence d’un ajout de cohérence. Bien que la taille des assemblées enregistrées soit relativement faible, cette étude suggère néanmoins une intrigante spécificité fonctionnelle entre neurones interagissant dans une assemblée en réponse à une stimulation visuelle. Cette étude peut être considérée comme une prémisse à la modélisation informatique à grande échelle de connectomes fonctionnels. / Background ‘Connectomics’— the mapping of neural connections, is a rapidly advancing field in neurosciences and it promises significant insights into the brain functioning. The formation of neuronal circuits in response to the sensory environment is an emergent property of the brain; however, the knowledge about the precise nature of these sub-networks is still limited. Even at the level of the visual cortex, which is the most studied area in the brain, how sensory inputs are processed between its neurons, is a question yet to be completely explored. Heuristically, this invites an investigation into the emergence of micro-circuits in response to a visual input — that is, how the intriguing interplay between a stimulus and a cell assembly is engineered and modulated? Methods Neuronal assemblies were recorded in response to randomly presented drifting sine-wave gratings in the layer II/III (area 17) of the primary visual cortex (V1) in anaesthetized cats using tungsten multi-electrodes. Cross-correlograms (CCGs) between simultaneously recorded neural activities were computed to reveal the functional links between neurons that were indicative of putative synaptic connections between them. Further, the peristimulus time histograms (PSTH) of neurons were compared to divulge the epochal synergistic collaboration in the revealed functional networks. Thereafter, perievent spectrograms were computed to observe the gamma oscillations in emergent microcircuits. Noise correlation (Rsc) was calculated for the connected and unconnected neurons within these microcircuits. Results The functionally linked neurons collaborate synergistically with augmented activity in a 50-ms window of opportunity compared with the functionally unconnected neurons suggesting that the connectivity between neurons leads to the added excitability between them. Further, the perievent spectrogram analysis revealed that the connected neurons had an augmented power of gamma activity compared with the unconnected neurons in the emergent 50-ms window of opportunity. The low-band (20-40 Hz) gamma activity was linked to the regular-spiking (RS) neurons, whereas the high-band (60-80 Hz) activity was related to the fast-spiking (FS) neurons. The functionally connected neurons systematically displayed higher Rsc compared with the unconnected neurons in emergent microcircuits. Finally, the CCG analysis revealed that there is an activation of a salient functional network in an assembly in relation to the presented orientation. Closely tuned neurons exhibited more connections than the distantly tuned neurons. Untuned assemblies did not display functional linkage. In short, a ‘signature’ functional network was formed between neurons comprising an assembly that was strictly related to the presented orientation. Conclusion Indeed, this study points to the fact that a cell-assembly is the fundamental functional unit of information processing in the brain, rather than the individual neurons. This dilutes the importance of a neuron working in isolation, that is, the classical firing rate paradigm that has been traditionally used to study the encoding of a stimulus. This study also helps to reconcile the debate on gamma oscillations in that they systematically originate between the connected neurons in assemblies. Though the size of the recorded assemblies in the current investigation was relatively small, nevertheless, this study shows the intriguing functional specificity of interacting neurons in an assembly in response to a visual input. One may form this study as a premise to computationally infer the functional connectomes on a larger scale.

Les chauve-souris représentant un réservoir important pour de nombreux virus pathogènes pour l’homme, un ensemble d’études en évolution moléculaire converge vers l’évidence d’une forte pression de sélection au niveau de gènes impliqués dans l’immunité dans l’ordre Chiroptera. En particulier, les chauves-souris du genre Pteropus hébergent des virus de la famille Henipavirus: Nipah et Hendra. Ces virus sont responsables d'épidémies en Asie du sud-est, et bien qu'ayant un taux d'incidence bas, les maladies résultantes de l'infection ont un taux de létalité allant de 40% à 90% chez l'homme. L’infection atteint aussi la plupart des mammifères avec des symptômes clinique graves, (e.g. porc ou cheval : espèces d’intérêt agronomique). La particularité du genre Pteropus est de ne pas développer ces symptômes cliniques graves d’infection. Afin d'en identifier les bases génétiques, nous avons utilisé l'analyse de sélection positive sur l’ensemble du génome codant sans restreindre notre analyse aux gènes de l’immunité. Nous avons mis en place les outils informatiques innovants et nécessaires au déploiement de cette démarche. Ces analyses, reposent sur des séquences de références pour les génomes de différentes espèces, et en absence du génome de référence pour P. giganteus, nous l’avons préalablement séquencé et assemblé. Or, tous les gènes sous sélection ne sont pas forcément liés à notre phénotype d’intérêt mais possiblement à d’autres (e.g. capacité de vol). Nous avons mis en place un algorithme afin d’établir un lien fonctionnel potentiel entre ces gènes identifiés sous sélection positive et un phénotype d’intérêt. / Bats represent a considerable reservoir for an extensive group of human pathogenic viruses. A number of molecular evolution studies points toward the evidence of a strong selection pressure in Chiroptera immune-related genes. Notably, Pteropus bats host viruses from Henipavirus genus: Nipah and Hendra. These viruses are responsible for epidemics in South-Est Asia, and, while the incidence is low, the resulting diseases are highly lethal, ranging between 40 to 90% in humans. Most of mammals are susceptible to the infection (including pigs and horses, animals valued in agronomy), and develop severe clinical symptoms. Specificity of Pteropus genus lies in the absence of clinical symptoms following the infection. In order to identify the genetic basis of this interesting phenomenon, we applied positive selection analysis to the entire coding genome, without bounding our analysis to immune-regulating genes. We have set breakthrough computational tools, without which our analysis would not have been possible. Reference sequences from genome of several species are the groundwork for our analysis. As P. giganteus reference genome has not yet been resolved, we sequenced and assembled it. However, not all genes under positive selection are necessarily linked to a phenotype of interest, but may be linked to other phenotypes (such as the flying ability). We have thus developed an algorithm to establish a possible functional link between the genes identified under positive selection and a phenotype of interest, which allows new perspectives in phylogenomic research.

Ce travail a porté sur les remaniements corticaux précoces des circuits moteurs et extramoteurs dans la SLA grâce à l’étude des activations IRMf issues de tâches motrices et cognitives. La première partie de nos travaux nous a permis de montrer grâce des tâches simples d’activation motrice en IRMf (1) qu’alors que les patients SLA présentaient un déficit moteur discret, une augmentation des activations corticales est apparue dans les aires sensorimotrices bilatérales du cerveau. (2) Ces modifications précoces de l’activité neuronale étaient corrélées à la latéralisation du déficit moteur du membre ou la prédominance manuelle et surtout, (3) au taux de progression de la maladie à un an et à la survie, suggérant que ce remaniement de l’activité qui correspond probablement à de la plasticité cérébrale a des implications fonctionnelles. Enfin, (4) ce phénomène apparaissait actif puisqu’il s’est poursuivi pendant au moins onze mois. Dans la deuxième partie, nous avons montré lors d’une tâche de fluence verbale silencieuse que (1) les SLA présentaient une suractivation initiale des aires dévolues au processus sémantique avec un renforcement de la connectivité fonctionnelle entre les réseaux (CFR). (2) Au bout de onze mois et alors que l’atteinte des fluences était stable, ce phénomène de compensation s’épuisait avec une diminution conjointe de l’activation des réseaux et de la CFR. La tâche de 2-Back, réalisée alors que les patients ne présentaient pas d’atteinte de la mémoire de travail lors des tests psychométriques, nous a permis de montrer en outre que certains circuits non-moteurs se réorganisaient très précocement chez les patients, alors même qu’ils étaient asymptomatiques / In this work we used motor and cognitive tasks in an fMRI study to explore the early cortical reorganizations of the motor and extra-motor circuits in ALS patients. In a first part, using a simple motor task, we demonstrated (1) that increased cortical BOLD signal changes occurred in specific regions of the brain of ALS patients when their motor deficit was still moderate, and that this early signal changes correlated with (2) the lateralisation of the motor deficit or hand predominance and, more importantly, (3) with the rate of disease progression at one year and survival time, suggesting that modulations of cerebral activity in ALS may have functional implications. Furthermore, (4) this brain plasticity was maintained with time and disease progression during at least eleven months. In a second part, we demonstrated during a silent verbal fluency task (1) that ALS patients presented initially an increased cortical activation of areas devolved to the semantic process with an intensification of the functional network connectivity (FNC). (2) After eleven months and while their performance in tests of verbal fluency was stable, this cerebral compensation ran out with a decrease of the previous cerebral activations and the FNC. A N-back working memory paradigm, realized while the patients did not present any deficit of their working memory, allowed us to show that certain non-motor circuits were reorganized prematurely while patients were still asymptomatic

While task-based functional magnetic resonance imaging (fMRI) has helped us understand the functional role of many regions in the human brain, many diseases and complex behaviors defy explanation. Alternatively, if no task is performed, the fMRI signal between distant, anatomically connected, brain regions is similar over time. These correlations in “resting state” fMRI have been strongly linked to behavior and disease. Previous work primarily calculated correlation in entire fMRI runs of six minutes or more, making understanding the neural underpinnings of these fluctuations difficult. Recently, coordinated dynamic activity on shorter time scales has been observed in resting state fMRI: correlation calculated in comparatively short sliding windows and quasi-periodic (periodic but not constantly active) spatiotemporal patterns. However, little relevance to behavior or underlying neural activity has been demonstrated. This dissertation addresses this problem, first by using 12.3 second windows to demonstrate a behavior-fMRI relationship previously only observed in entire fMRI runs. Second, simultaneous recording of fMRI and electrical signals from the brains of anesthetized rats is used to demonstrate that both types of dynamic activity have strong correlates in electrophysiology. Very slow neural signals correspond to the quasi-periodic patterns, supporting the idea that low-frequency activity organizes large scale information transfer in the brain. This work both validates the use of dynamic analysis of resting state fMRI, and provides a starting point for the investigation of the systemic basis of many neuropsychiatric diseases.