En France, la nécessaire rationalisation des moyens alloués aux hôpitaux a abouti à une concentration des ressources et une augmentation de la complexité des plateaux techniques. Leur pilotage et leur répartition territoriale s’avèrent d’autant plus difficile, soulevant ainsi la problématique de l’optimisation des systèmes de soins. L’utilisation des données massives produites pas ces systèmes pourrait constituer une nouvelle approche en matière d’analyse et d’aide à la décision. Méthode : A partir d’une réflexion sur la notion de performance, différentes approches d’optimisation préexistantes sont d’abord mis en évidence. Le bloc opératoire a été choisi en tant que terrain expérimental. Suit une analyse sur une fusion d’établissements en tant qu’exemple d’une approche d’optimisation par massification.Ces deux étapes permettent de défendre une approche alternative qui associe l’usage de données massives, la science des réseaux et la visualisation des données sous forme cartographique. Deux sets de séjours en chirurgie orthopédique sur la région ex-Midi-Pyrénées sont utilisés. L’enchainement des séjours de soins est considéré en tant en réseau de données. L’ensemble est projeté dans un environnement visuel développé en JavaScript et permettant une fouille dynamique du graphe. Résultats : La possibilité de visualiser des parcours de santé sous forme de graphes NŒUDS-LIENS est démontrée. Les graphes apportent une perception supplémentaire sur les enchainements de séjours et les redondances des parcours. Le caractère dynamique des graphes permet en outre leur fouille. L’approche visuelle subjective est complétée par une série de mesures objectives issues de la science des réseaux. Les plateaux techniques de soins produisent des données massives utiles à leur analyse et potentiellement à leur optimisation. La visualisation graphique de ces données associées à un cadre d’analyse tel que la science des réseaux donne des premiers indicateurs positifs avec notamment la mise en évidence de motifs redondants. La poursuite d’expérimentations à plus large échelle est requise pour valider, renforcer et diffuser ces observations et cette méthode. / In France, the streamlining of means assigned hospitals result in concentration of resources ana growing complexily of heallhcare facilities. Piloting and planning (them turn out to be all the more difficult, thus leading of optimjzation problems. The use of massive data produced by these systems in association with network science an alternative approach for analyzing and improving decision-making support jn healthcare. Method : Various preexisting optimisation are first highblighted based on observations in operating theaters chosen as experirnentai sites. An analysis of merger of two hospitlas also follows as an example of an optimization method by massification. These two steps make it possible to defend an alternative approach that combines the use of big data science of networks data visualization techniques. Two sets of patient data in orthopedic surgery in the ex-Midi-Pyrénées region in France are used to create a network of all sequences of care. The whole is displayed in a visual environment developed in JavaScript allowing a dynamic mining of the graph. Results: Visualizing healthcare sequences in the form of nodes and links graphs has been sel out. The graphs provide an additional perception of' the redundancies of he healthcare pathways. The dynamic character of the graphs also allows their direct rnining. The initial visual approach is supplernented by a series of objcctive measures from the science of networks. Conciusion: Healthcare facilities produce massive data valuable for their analysis and optimization. Data visualizalion together with a framework such as network science gives prelimiaary encouraging indicators uncovering redondant healthcare pathway patterns. Furthev experimentations with various and larger sets of data is required to validate and strengthen these observations and methods.

This thesis investigates the role of network structure, brokerage, and framing in high-risk collective action. I use the protest movement that emerged in Russia following falsified national elections in 2011 and 2012 as an empirical case study. I draw on a unique dataset of nearly 30,000 online documents and the linking structure of over 3,500 Russian Web sites. I employ a range of computational social science methods, including Exponential Random Graph Modeling, an advanced statistical model for social networks, social network analysis, machine learning, and latent semantic analysis. I address three research questions in this thesis. The first asks if a protest network challenging a hybrid regime will have a polycentric or hierarchical structure, and if that structure changes over time. Polycentric networks are conducive to high-risk collective action and are robust to the targeted removal of key nodes, while hierarchical networks can more easily mobilize protesters and spread information. I find that the Russian protest network has a polycentric structure only at the beginning of the protests, and moves towards a less effective hierarchical structure as the movement loses popular support. The second research question seeks to understand if brokered text is actually novel, and if that text is more novel in polycentric networks than in hierarchical ones. Brokers are the individuals or nodes in a network that connect disparate groups through weak ties and close structural holes. Brokers are advantageous because they have access to and spread novel information. I find that the text among nodes in brokered relationships is indeed novel, but that information novelty decreases when networks have a hierarchical structure. The last research question asks if a protest movement in a high-risk political setting can be more successful than the government at spreading its preferred frames, and within such a movement, whether moderate or extremist framing is more prevalent. I find that the opposition is far more effective than the government in spreading its frames, even when the government organizes massive counter protests. Within the movement, moderates are more likely to have their framing adopted online than extremists, unless violence occurs at protests. The findings suggest that movements should build flatter, more diffuse networks by ensuring that brokers tie together diverse protest constituencies. The findings also provide evidence against those who claim that authoritarian governments are more effective in shaping online discourse than oppositional movements, and also suggest that movements should advance moderate framing in order to attract a wider base of support among the general population.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2014-09-11T11:50:01Z No. of bitstreams: 2 Dissertacao Diego Americo Guedes.pdf: 971567 bytes, checksum: a39a61e190ff600e318da0dd24eb108c (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-09-11T11:50:01Z (GMT). No. of bitstreams: 2 Dissertacao Diego Americo Guedes.pdf: 971567 bytes, checksum: a39a61e190ff600e318da0dd24eb108c (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In this work we present and evaluate a modeling methodology that describes the creation of a topology for wireless mesh networks, and how this topology changes over time. The modeling methodology is based on network science, which is a multidisciplinary research area that has a lot of tools to help in the study and analysis of networks. In wireless mesh networks, the relative importance of the nodes is often related to the topological aspects, and data flow. However, due to the dynamics of the network, the relative importance of the nodes may vary in time. In the context of network science, the concept of centrality metric represents the relative importance of a node in the network. In this work we show also that the current centrality metrics are not able to rank properly the nodes in wireless mesh networks. Then we propose a new metric of centrality that ranks the most important nodes in a wireless mesh network over time. We evaluate our proposal using data from a case study of the proposed modeling methodology and also from real wireless mesh networks, achieving satisfactory performance. The characteristics of our metric make it a useful tool for monitoring dynamic networks. / Neste trabalho, apresentamos e avaliamos uma modelagem que descreve a criação de uma topologia para redes em malha sem fio e como essa se altera no tempo. A modelagem é baseada em ciência das redes (network science), uma área multidisciplinar de pesquisa que possui uma grande quantidade de ferramentas para auxiliar no estudo e análise de redes. Em redes em malha sem fio, a importância relativa dos nós é frequentemente relacionada a aspectos topológicos e ao fluxo de dados. Entretanto, devido à dinamicidade da rede, a importância relativa de um nó pode variar no tempo. No contexto de ciência de redes, o conceito de métricas de centralidade reflete a importância relativa de um nó na rede. Neste trabalho, mostramos também que as métricas atuais de centralidade não são capazes de classificar de maneira adequada os nós em redes em malha sem fio. Propomos então uma nova métrica de centralidade que classifica os nós mais importantes em uma rede em malha sem fio ao longo do tempo. Avaliamos nossa proposta com dados obtidos de um estudo de caso da modelagem proposta e de redes em malha sem fio reais, obtendo desempenho satisfatório. As características da nossa métrica a tornam uma ferramenta útil para monitoramento de redes dinâmicas.

Sistemas Complexos são característicos por possuir uma rede interna representando o relacionamento estrutural entre seus elementos e uma forma natural de interpretar essa interação é através de um grafo. Neste trabalho, o sistema de transporte público urbano de São Paulo é reinterpretado de forma acoplada (ônibus e metrô juntos) como uma rede complexa, abstraindo detalhes operacionais e focando na conectividade. Pelo grafo empiricamente gerado, é feita uma caraterização estatística nas métricas de redes complexas, onde diferentes valores de raio de distância são usados para agrupar pontos e estações próximas que antes se apresentavam desconectados. Esse agrupamento pode ser interpretado como uma ferramenta de política pública, representando a disposição do usuário em se locomover ao ponto mais próximo para acessar o transporte. O processo mostrou que aumentar essa disposição gera grande redução na distância e número de passos entre ônibus, trens e linhas de metrô para atingir todos os destinos da rede. É utilizado um modelo exploratório que testa a robustez da rede aleatoriamente, deterministicamente e probabilisticamente tendo como alvo pontos e linhas. De acordo com os raios de agrupamento, definido como disposição, diferentes valores de fragmentação foram obtidos diante dos ataques simulados. Esses resultados suportam duas principais características observadas na literatura de redes deste tipo: possuem um elevado grau de robustez à falhas aleatórias, mas são vulneráveis a ataques tendo como alvo nós ou links importantes / Complex systems are characteristic by having an internal network representing the structural relationship between its elements and a natural way to interpret this interaction is through a graph. In this work, the urban public transport system of São Paulo is reinterpreted as a coupled (bus and subway) complex network, bypassing operational details and focusing on connectivity. Using the empirically generated graph, a statistical characterisation is made by network metrics where different radius values are used to group nearby stops and stations that were disconnected before. That can be interpreted as a public policy tool, representing the user\'s willingness to get around the nearest point to access transportation. This process has shown that increasing this willingness generates great reduction in the distance and in the number of jumps between buses, trains and subways lines to achieve all the network destinations. An exploratory model is used to test the robustness of the network by randomly, deterministically and preferentially targeting the stops and service lines. According to the grouping radius, aka willingness, different fragmentation values were obtained under attack simulations. These findings support two main characteristics observed in such networks literature: they have a high degree of robustness to random failures, but are vulnerable to targeted attacks

Dans le contexte des réseaux complexes, la structure communautaire du réseau devient un sujet important pour plusieurs domaines de recherche. Les communautés sont en général vues comme des groupes intérieurement denses. La détection de tels groupes offre un éclairage intéressant sur la structure du réseau. Par exemple, une communauté de pages web regroupe des pages traitant du même sujet. La définition de communautés est en général limitée à une partition de l’ensemble des nœds. Cela exclut par définition qu’un nœd puisse appartenir à plusieurs communautés, ce qui pourtant est naturel dans de nombreux (cas des réseaux sociaux par exemple). Une autre question importante et sans réponse est l’étude des réseaux et de leur structure communautaire en tenant compte de leur dynamique. Cette thèse porte sur l’étude de réseaux dynamiques et la détection de communautés recouvrantes. Nous proposons deux méthodes différentes pour la détection de communautés recouvrantes. La première méthode est appelée optimisation de clique. L'optimisation de clique vise à détecter les nœds recouvrants granulaires. La méthode de l'optimisation de clique est une approche à grain fin. La seconde méthode est nommée détection floue (fuzzy detection). Cette méthode est à grain plus grossier et vise à identifier les groupes recouvrants. Nous appliquons ces deux méthodes à des réseaux synthétiques et réels. Les résultats obtenus indiquent que les deux méthodes peuvent être utilisées pour caractériser les nœds recouvrants. Les deux approches apportent des points de vue distincts et complémentaires. Dans le cas des graphes dynamiques, nous donnons une définition sur la relation entre les communautés à deux pas de temps consécutif. Cette technique permet de représenter le changement de la structure en fonction du temps. Pour mettre en évidence cette relation, nous proposons des diagrammes de lignage pour la visualisation de la dynamique des communautés. Ces diagrammes qui connectent des communautés à des pas de temps successifs montrent l’évolution de la structure et l'évolution des groupes recouvrantes., Nous avons également appliquer ces outils à des cas concrets. / In complex networks, the notion of community structure refers to the presence of groups of nodes in a network. These groups are more densely connected internally than with the rest of the network. The presence of communities inside a network gives an insight on network structural properties. For example, in social networks, communities are based on common interests, location, hobbies.... Generally, a community structure is described by a partition of the network nodes, where each node belongs to a unique community. A more reasonable description seems to be overlapping community structure, where nodes are allowed to be shared by several communities. Moreover, when considering dynamic networks whose interactions between nodes evolve in time, it appears crucial to consider also the evolution of the intrinsic community structure. This thesis focus on mining dynamic community evolution and overlapping community detection. We have proposed two distinct methods for overlapping community detection. The first one named clique optimization and the second one called fuzzy detection. Our clique optimization aims to identify granular overlaps and it is a fine grain scale approach. Our fuzzy detection is at a coarser grain scale with the strategy of identifying modular overlaps. Their applications in synthetic and real networks indicate that both methods can be used for characterizing overlapping nodes but in distinct and complementary views. We also propose the definition of predecessor and successor in mining community evolution. Such definition describes the relationship between communities at different time steps. We use it to detect community evolution in dynamic networks and show how modular overlaps evolve over time. A visualization tool called lineage diagrams is used to show community evolution by connecting communities in relationship of predecessor and successor. Several cases are studied.

El cerebro está constituido por numerosos elementos que se encuentran interconectados de forma masiva y organizados en módulos que forman redes jerárquicas. Ciertas patologías cerebrales, como la enfermedad de Alzheimer y el trastorno por consumo de alcohol, se consideran el resultado de efectos en cascada que alteran la conectividad cerebral. La presente tesis tiene como objetivo principal la aplicación de las técnicas de análisis de la ciencia de redes para el estudio de las redes estructurales y funcionales en el cerebro, tanto en un estado control como en un estado patológico. Así, en el primer estudio de la presente tesis se examina la relación entre la conectividad estructural y funcional en la corteza cerebral de la rata. Se lleva a cabo un análisis comparativo entre las conexiones estructurales en la corteza cerebral de la rata y los valores de correlación calculados sobre las mismas regiones. La información acerca de la conectividad estructural se ha obtenido a partir de estudios previos, mientras que la conectividad funcional se ha calculado a partir de imágenes de resonancia magnética funcional. Determinadas propiedades topológicas, y extraídas de la conectividad estructural, se relacionan con la organización modular de las redes funcionales en estado de reposo. Los resultados obtenidos en este primer estudio demuestran que la conectividad estructural y funcional cortical están altamente relacionadas entre sí. Estudios recientes sugieren que el origen de la enfermedad de Alzheimer reside en un mecanismo en el cual depósitos de ovillos neurofibrilares y placas de beta-amiloide se acumulan en ciertas regiones cerebrales, y tienen la capacidad de diseminarse por el cerebro actuando como priones. En el segundo estudio de la presente tesis se investiga si las redes estructurales que se generan con la técnica de resonancia magnética ponderada en difusión podrían ser de utilidad para el diagnóstico de la pre-demencia causada por la enfermedad de Alzheimer. Mediante el uso de imágenes procedentes de la base de datos ADNI, se aplican técnicas de aprendizaje máquina con el fin de identificar medidas de centralidad que se encuentran alteradas en la demencia. En la segunda parte del estudio, se utilizan imágenes procedentes de la base de datos NKI para construir un modelo matemático que simule el proceso de envejecimiento normal, así como otro modelo que simule el proceso de desarrollo de la enfermedad. Con este modelado matemático, se pretende estimar la etapa más temprana que está asociada con la demencia. Los resultados obtenidos de las simulaciones sugieren que en etapas tempranas de la enfermedad de Alzheimer se producen alteraciones estructurales relacionados con la demencia. La cuantificación de la relación estadística entre las señales BOLD de diferentes regiones puede informar sobre el estado funcional cerebral característico de enfermedades neurológicas y psiquiátricas. En el tercer estudio de la presente tesis se estudian las alteraciones en la conectividad funcional que tienen lugar en ratas dependientes del consumo de alcohol cuando se encuentran en estado de reposo. Para ello, se ha aplicado el método NBS. El análisis de este modelo de rata revela diferencias estadísticamente significativas en una subred de regiones cerebrales que están implicadas en comportamientos adictivos. Por lo tanto, estas estructuras cerebrales podrían ser el foco de posibles dianas terapéuticas. La tesis aporta tres innovadoras contribuciones para entender la conectividad cerebral bajo la perspectiva de la ciencia de redes, tanto en un estado control como en un estado patológico. Los resultados destacan que los modelos basados en las redes cerebrales permiten esclarecer la relación entre la estructura y la función en el cerebro. Y quizás más importante, esta perspectiva de red tiene aplicaciones que se podrían trasladar a la práctica clínica. / The brain is composed of massively connected elements arranged into modules that form hierarchical networks. Experimental evidence reveals a well-defined connectivity design, characterized by the presence of strategically connected core nodes that critically contribute to resilience and maintain stability in interacting brain networks. Certain brain pathologies, such as Alzheimer's disease and alcohol use disorder, are thought to be a consequence of cascading maladaptive processes that alter normal connectivity. These findings have greatly contributed to the development of network neuroscience to understand the macroscopic organization of the brain. This thesis focuses on the application of network science tools to investigate structural and functional brain networks in health and disease. To accomplish this goal, three specific studies are conducted using human and rodent data recorded with MRI and tracing technologies. In the first study, we examine the relationship between structural and functional connectivity in the rat cortical network. Using a detailed cortical structural matrix obtained from published histological tracing data, we first compare structural connections in the rat cortex with their corresponding spontaneous correlations extracted empirically from fMRI data. We then show the results of this comparison by relating structural properties of brain connectivity to the functional modularity of resting-state networks. Specifically, we study link reciprocity in both intra- and inter-modular connections as well as the structural motif frequency spectrum within functionally defined modules. Overall, our results provide further evidence that structural connectivity is coupled to and shapes functional connectivity in cortical networks. The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting pahtogenic seeding and subsequent prion-like spreading processes of neurofibrillary tangles and amyloid plaques. In the second study of this thesis, we investigate whether structural brain networks as measured with dMRI could serve as a complementary diagnostic tool in prodromal dementia. Using imaging data from the ADNI database, we first aim to implement machine learning techniques to extract centrality features that are altered in Alzheimer's dementia. We then incorporate data from the NKI database and create dynamical models of normal aging and Alzheimer's disease to estimate the earliest detectable stage associated with dementia in the simulated disease progression. Our model results suggest that changes associated with dementia begin to manifest structurally at early stages. Statistical dependence measures computed between BOLD signals can inform about brain functional states in studies of neurological and psychiatric disorders. Furthermore, its non-invasive nature allows comparable measurements between clinical and animal studies, providing excellent translational capabilities. In the last study, we apply the NBS method to investigate alterations in the resting-state functional connectivity of the rat brain in a PD state, an established animal model of clinical relevant features in alcoholism. The analysis reveal statistically significant differences in a connected subnetwork of structures with known relevance for addictive behaviors, hence suggesting potential targets for therapy. This thesis provides three novel contributions to understand the healthy and pathological brain connectivity under the perspective of network science. The results obtained in this thesis underscore that brain network models offer further insights into the structure-function coupling in the brain. More importantly, this network perspective provides potential applications for the diagnosis and treatment of neurological and psychiatric disorders. / El cervell està constituït per nombrosos elements que es troben interconnectats de forma massiva i organitzats en mòduls que formen xarxes jeràrquiques. Certes patologies cerebrals, com la malaltia d'Alzheimer i el trastorn per consum d'alcohol, es consideren el resultat d'efectes en cascada que alteren la connectivitat cerebral. La present tesi té com a objectiu principal l'aplicació de les tècniques d'anàlisi de la ciència de xarxes per a l'estudi de les xarxes estructurals i funcionals en el cervell, tant en un estat control com en un estat patològic. Així, en el primer estudi de la present tesi s'examina la relació entre la connectivitat estructural i funcional en l'escorça cerebral de la rata. Es du a terme una anàlisi comparativa entre les connexions estructurals en l'escorça cerebral de la rata i els valors de correlació calculats sobre les mateixes regions. La informació sobre la connectivitat estructural s'ha obtingut a partir d'estudis previs, mentre que la connectivitat funcional s'ha calculat a partir d'imatges de ressonància magnètica funcional. Determinades propietats topològiques, i extretes de la connectivitat estructural, es relacionen amb l'organització modular de les xarxes funcionals en estat de repòs. Els resultats obtinguts en este primer estudi demostren que la connectivitat estructural i funcional cortical estan altament relacionades entre si. Estudis recents suggereixen que l'origen de la malaltia d'Alzheimer resideix en un mecanisme en el qual depòsits d'ovulets neurofibrilars i plaques de beta- miloide s'acumulen en certes regions cerebrals, i tenen la capacitat de disseminar-se pel cervell actuant com a prions. En el segon estudi de la present tesi s'investiga si les xarxes estructurals que es generen amb la tècnica de la imatge per ressonància magnètica ponderada en difusió podrien ser d'utilitat per al diagnòstic de la predemència causada per la malaltia d'Alzheimer. Per mitjà de l'ús d'imatges procedents de la base de dades ADNI, s'apliquen tècniques d'aprenentatge màquina a fi d'identificar mesures de centralitat que es troben alterades en la demència. En la segona part de l'estudi, s'utilitzen imatges procedents de la base de dades NKI per a construir un model matemàtic que simule el procés d'envelliment normal, així com un altre model que simule el procés de desenrotllament de la malaltia. Amb este modelatge matemàtic, es pretén estimar l'etapa més primerenca que està associada amb la demència. Els resultats obtinguts de les simulacions suggereixen que en etapes primerenques de la malaltia d'Alzheimer es produeixen alteracions estructurals relacionats amb la demència. La quantificació de la relació estadística entre els senyals BOLD de diferents regions pot informar sobre l'estat funcional cerebral característic de malalties neurològiques i psiquiàtriques. A més, a causa de la seua naturalesa no invasiva, és possible comparar els resultats obtinguts entre estudis clínics i estudis amb animals d'experimentació. En el tercer estudi de la present tesi s'estudien les alteracions en la connectivitat funcional que tenen lloc en rates dependents del consum d'alcohol quan es troben en estat de repòs. Per a realitzar-ho, s'ha aplicat el mètode NBS. L'anàlisi d'aquest model de rata revela diferències estadísticament significatives en una subxarxa de regions cerebrals que estan implicades en comportaments addictius. Per tant, estes estructures cerebrals podrien ser el focus de possibles dianes terapèutiques. La tesi aporta tres innovadores contribucions per a entendre la connectivitat cerebral davall la perspectiva de la ciència de xarxes, tant en un estat control com en un estat patològic. Els resultats destaquen que els models basats en les xarxes cerebrals permeten aclarir la relació entre l'estructura i la funció en el cervell. I potser més important, esta perspectiva de xarxa té aplicacions que es podrien traslladar a la pràcti / Díaz Parra, A. (2018). A network science approach of the macroscopic organization of the brain: analysis of structural and functional brain networks in health and disease [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/106966 / TESIS

Comprendre le câblage complexe de la régulation cellulaire reste un défi des plus redoutables.Les connaissances fondamentales sur le câblage et le fonctionnement du réseau d’homéostasiedes protéines aideront à mieux comprendre comment l’homéostasie des protéines échouedans les maladies et comment les modèles de régulation du réseau d’homéostasie desprotéines peuvent être ciblés pour une intervention thérapeutique. L’étude vise à développeret à appliquer une nouvelle méthodologie de calcul pour l’identification systématique etla caractérisation des systèmes de rétroaction en homéostasie des protéines. La rechercheproposée combine des idées et des approches issues de la science des protéines, de la biologiedes systèmes de levure, de la biologie computationnelle et de la biologie des réseaux.La difficulté dans la tâche d’incorporer des données multi-plateformes multi-omiques estamplifiée par le vaste réseau de gènes, protéines et métabolites interconnectés qui seréunissent pour remplir une fonction spécifique. Pour ma thèse de maîtrise, j’ai développéun algorithme PBPF (Path-Based Pattern Finding), qui recherche et énumère les motifsde réseau de la topologie requise. Il s’agit d’un algorithme basé sur la théorie des graphesqui utilise la combinaison d’une méthode transversale de profondeur et d’une méthodede recherche par largeur ensuite pour identifier les topologies de sous-graphes de réseaurequises. En outre, le fonctionnement de l’algorithme a été démontré dans les domainesde l’homéostasie des protéines chezSaccharomyces cerevisiae. Une approche systématiqued’intégration des données de la biologie des systèmes a été orchestrée, qui montre l’iden-tification systématique de motifs de rétroaction régulatrice connus dans l’homéostasie desprotéines. Il revendique fortement la capacité d’identifier de nouveaux motifs de rétroactionréglementaire envahissants. L’application de l’algorithme peut être étendue à d’autressystèmes biologiques, par exemple, pour identifier des motifs de rétroaction spécifiques àl’état cellulaire dans le cas de cellules souches. / Understanding the intricate wiring of cellular regulation remains a most formidable chal-lenge. The fundamental insights into the wiring and functioning of the protein homeostasisnetwork will help to better understand how protein homeostasis fails in diseases and howthe regulatory patterns of protein homeostasis network can be targeted for therapeuticintervention. The study aims at developing and applying novel computational methodologyfor the systematic identification and characterization of feedback systems in proteinhomeostasis. The proposed research combines ideas and approaches from protein science,yeast systems biology, computational biology, as well as network biology. The difficultyin the task of incorporating multi-platform multi-omics data is amplified by the largenetwork of inter-connected genes, proteins and metabolites that come together to perform aspecific function. For my master’s thesis, I developed a path-based pattern finding (PBPF)algorithm, which searches and enumerates network motifs of required topology. It is a graphtheory based algorithm which utilizes the combination of depth-first transverse method andbreadth-first search method to identify the required network sub-graph topologies. Further,the functioning of the algorithm has been demonstrated in the realms of protein homeostasisinSaccharomyces cerevisiae. A systematic approach of integration of systems biologydata has been orchestrated, which shows the systematic identification of known regulatoryfeedback motifs in protein homeostasis. It claims the unique ability to identify novelpervasive regulatory feedback motifs. The application of the algorithm can be extended toother biological systems, for example, to identify cell-state specific feedback motifs in caseof stem-cells.

A biological function is rarely accomplished by a single gene. More often, proteins come together in complexes, and it is their collaboration within a complex that enables the associated biological function. However, the current map of the interactome is incomplete, meaning we have not observed all the interactions occurring in the cell yet. Gold standard experimental methods for the determination of all the protein-protein interactions (PPIs) in human interactome are time-consuming, expensive and may not even be feasible considering the vast number of protein pairs that need to be tested. For decades, scientists and engineers dedicated their efforts to forecasting protein interactions, predominantly relying on network topology methods. However, the emergence of AlphaFold2 intelligence has redefined the computational biology field by harnessing 3D molecular structural data to predict interacting protein in complexes, offering a promising alternative to traditional laboratory experiments. It is in this context that we introduce an innovative concept known as Network Shape Intelligence (NSI). It is the intelligence displayed by any topological network automata to perform valid connectivity predictions without training, but only processing the input knowledge associated to the local topological network organization. NSI transcends conventional link prediction methods by weaving together principles inspired by brain network science. It achieves this by minimizing external links within local communities, a strategy founded on local topology and plasticity principles initially developed for brain networks but subsequently extended to diverse complex networks. In addition to the incompleteness of the PPI network, the question of the reliability of the existing wealth of information through observed physical links also arises. Therefore, to evaluate the performance of a predictor we must make sure that the tested positive and negative interactions are reliable. We introduce the Bona Fide Evaluation Methodology (BFEM). The rigor of protein interaction predictions is ensured through a balanced classification scenario, meticulously constructed using the well-studied yeast protein interactome. Our methodology focuses on creating a golden standard set of true and false interactions, enhancing the reliability of our evaluations. We show that by using only local network information and without the need for training, these network automata designed for modelling and predicting network connectivity can outperform AlphaFold2 intelligence in vanilla protein interactions prediction. We find that the set of interactions mispredicted by AlphaFold2 predominantly consists of proteins whose amino acids exhibit higher probability of being associated with intrinsically disordered regions. Finally, we suggest that the future advancements in AlphaFold intelligence could integrate principles of NSI to further enhance the modelling and structural prediction of protein interactions.

Public infrastructure systems are crucial components of modern urban communities as they play major roles in elevating countries’ socio-economics. However, the inherent complexity and systemic interdependence of infrastructure construction/renewal projects have left sites hindered with multiple forms of performance disruptions (e.g., schedule delays, cost overruns, workplace injuries) that result in long-term consequences such as claims, disputes, and stakeholder dissatisfactions. The evolution of advanced data-driven tools (e.g., machine learning and complex network analytics) can play a pivotal role in driving improvements in the management strategies of complex projects due to such tools’ usefulness in applications related to interdependent systems. In this respect, the research presented in this dissertation is aimed at developing data-driven strategies geared towards a resilience-based approach to managing complex infrastructure projects. Such strategies can support project managers and stakeholders with data-informed decision-making to mitigate the impacts of systemic interdependence-induced risks at different levels of their projects. Specifically, the developed data-driven resilience-based strategies can empower decision-makers with the ability to: i) predict potential performance disruptions based on real-time and dynamic project conditions such that proactive response/mitigation strategies and/or contingencies can be deployed ahead of time; and ii) develop adaptive solutions against potential interdependence-induced cascade project disruptions such that rapid restoration of the most important set of performance targets can be restored. It is important to note that data-driven strategies and other analytics-based approaches are not proposed herein to replace but rather to complement the expertise and sensible judgment of project managers and the capabilities of available analysis tools. Specifically, the enriched predictive and analytical insights together with the proactive and rapid adaptation capabilities facilitated by the developed strategies can empower the new paradigm of resilience-guided management of complex dynamic infrastructure projects. / Thesis / Doctor of Philosophy (PhD)

<p dir="ltr">Manganese (Mn) is an essential trace element for humans that functions primarily as a coenzyme in several biological processes such as nerve and brain development, energy metabolism, bone growth and development, as well as cognitive functioning. However, overexposure to environmental Mn via occupational settings or contaminated drinking water can lead to toxic effects on the central nervous systems and cause a Parkinsonian disorder that features symptoms such as fine motor control deficits, dystonia rigidity, speech and mood disturbances, and cognitive deficits summarized under the term “manganism”. Over time, Mn exposure has shifted from acute, high-level instances leading to manganism, to low-level chronic exposure. Considering that Mn exposure is significantly lower than in the past, it is unlikely to expect manganism from chronic Mn exposure under current working conditions. Therefore, there is a need to develop sensitive methods to aid in updating the clinical diagnostic standards for manganism and Mn neurotoxicity as chronic exposure to Mn leads to more subtle symptoms.</p><p><br></p><p dir="ltr">Historically, magnetic resonance imaging (MRI) has been used as a non-invasive tool for detecting excess brain Mn accumulation. Specifically, T1-weighted images show bilateral hyperintensities of the globus pallidus (GP) due to the paramagnetic properties of Mn which increases the MR relaxation rate R1. Although the GP is considered the hallmark of excess brain Mn, this brain area is not necessarily associated with symptoms, exposure, or neuropsychological outcomes. Thus, the focus should not be on the GP only but on the entire brain. With recent advances in quantitative MRI (qMRI), whole brain mapping techniques allow for the direct measurement of relaxation rate changes due to Mn accumulation. The work in this dissertation uses such quantitative techniques and network science to establish novel computational in vivo imaging methods to a) visualize and quantify excess Mn deposition at the group and individual level, and b) characterize the toxicokinetics of excess brain Mn accumulation and the role of different brain regions in the development of neurotoxicity effects.</p><p><br></p><p dir="ltr">First, we developed a novel method for depicting excess Mn accumulation at the group level using high-resolution R1 relaxation maps to identify regional differences using voxel-based quantification (VBQ) and statistical parametric mapping. Second, we departed from a group analysis and developed subject-specific maps of excess brain Mn to gain a better understanding of the relationship between the spatial distribution of Mn and exposure settings. Third, we developed a novel method that combines network science with MRI relaxometry to characterize the storage and propagation of Mn and Fe in the human brain and the role of different brain regions in the development of neurotoxic effects. Lastly, we explore the application of ultra-short echo (UTE) imaging to map Fe content in the brain and compare it against R2* and quantitative susceptibility mapping (QSM).</p><p><br></p><p dir="ltr">Overall, this dissertation is a successful step towards establishing sensitive neuroimaging screening methods to study the effects of occupational Mn exposure. The individual Mn maps offer great potential for evaluating personal risk assessment for Mn neurotoxicity and allow monitoring of temporal changes in an individual, offering valuable information about the toxicokinetics of Mn. The integration of network science provides a holistic analysis to identify subtle changes in the brain’s mediation mechanisms of excess metal depositions and their associations with health outcomes.</p>