Ces dernières années, la puissance croissante des ordinateurs a permis à la fois de rassembler une quantité sans précédent de données décrivant la société moderne et d'envisager des outils numériques capables de s'attaquer à l'analyse et la modélisation les processus dynamiques qui se déroulent dans cette réalité complexe. Dans cette perspective, l'approche quantitative de la physique est un des catalyseurs de la croissance de nouveaux domaines interdisciplinaires visant à la compréhension des systèmes complexes techno-sociaux. Dans cette thèse, nous présentons dans cette thèse un cadre théorique et numérique pour simuler des épidémies de maladies infectieuses émergentes dans des contextes réalistes. Dans ce but, nous utilisons le rôle crucial de la mobilité des agents dans la diffusion des maladies infectieuses et nous nous appuyons sur l'étude des réseaux complexes pour gérer les ensembles de données à grande échelle décrivant les interconnexions de la population mondiale. En particulier, nous abordons deux différents problèmes de santé publique. Tout d'abord, nous considérons la propagation d’une épidémie au niveau mondial, et présentons un modèle de mobilité (GLEAM) conçu pour simuler la propagation d'une maladie de type grippal à l'échelle globale, en intégrant des données réelles de mobilité dans le monde entier. La dernière pandémie de grippe H1N1 2009 a démontré la nécessité de modèles mathématiques pour fournir des prévisions épidémiques et évaluer l'efficacité des politiques d'interventions. Dans cette perspective, nous présentons les résultats obtenus en temps réel pendant le déroulement de l'épidémie, ainsi qu'une analyse a posteriori portant sur les stratégies de lutte et sur la validation du modèle. Le deuxième problème que nous abordons est lié à la propagation de l'épidémie sur des systèmes en réseau dépendant du temps. En particulier, nous analysons des données décrivant les mouvements du bétail en Italie afin de caractériser les corrélations temporelles et les propriétés statistiques qui régissent ce système. Nous étudions ensuite la propagation d'une maladie infectieuse, en vue de caractériser la vulnérabilité du système et de concevoir des stratégies de contrôle. Ce travail est une approche interdisciplinaire qui combine les techniques de la physique statistique et de l'analyse des systèmes complexes dans le contexte de la mobilité des agents et de l'épidémiologie numérique. / In recent years, the increasing availability of computer power has enabled both to gather an unprecedented amount of data depicting the global interconnections of the modern society and to envision computational tools able to tackle the analysis and the modeling of dynamical processes unfolding on such a complex reality. In this perspective, the quantitative approach of Physics is catalyzing the growth of new interdisciplinary fields aimed at the understanding of complex techno-socio-ecological systems. By recognizing the crucial role of host mobility in the dissemination of infectious diseases and by leveraging on a network science approach to handle the large scale datasets describing the global interconnectivity, in this thesis we present a theoretical and computational framework to simulate epidemics of emerging infectious diseases in real settings. In particular we will tackle two different public health related issues. First, we present a Global Epidemic and Mobility model (GLEaM) that is designed to simulate the spreading of an influenza-like illness at the global scale integrating real world-wide mobility data. The 2009 H1N1 pandemic demonstrated the need of mathematical models to provide epidemic forecasts and to assess the effectiveness of different intervention policies. In this perspective we present the results achieved in real time during the unfolding of the epidemic and a posteriori analysis on travel related mitigation strategies and model validation. The second problem that we address is related to the epidemic spreading on evolving networked systems. In particular we analyze a detailed dataset of livestock movements in order to characterize the temporal correlations and the statistical properties governing the system. We then study an infectious disease spreading, in order to characterize the vulnerability of the system and to design novel control strategies. This work is an interdisciplinary approach that merges statistical physics techniques, complex and multiscale system analysis in the context of hosts mobility and computational epidemiology.

<div>This dissertation focuses on developing a series of mathematical models to understand the role of urban transportation system, urban mobility and information dissemination in the spreading process of infectious diseases within metropolitan areas. Urban transportation system serves as the catalyst of disease contagion since it provides the mobility for bringing people to participate in intensive urban activities and has high passenger volume and long commuting time which facilitates the spread of contagious diseases. In light of significant needs in understanding the connection between disease contagion and the urban transportation systems, both macroscopic and microscopic models are developed and the dissertation consists of three main parts. </div><div></div><div>The first part of the dissertation aims to model the macroscopic level of disease spreading within urban transportation system based on compartment models. Nonlinear dynamic systems are developed to model the spread of infectious disease with various travel modes, compare models with and without contagion during travel, understand how urban transportation system may facilitate or impede epidemics, and devise control strategies for mitigating epidemics at the network level. The hybrid automata is also introduced to account for systems with different levels of control and with uncertain initial epidemic size, and reachability analysis is used to over-approximate the disease trajectories of the nonlinear systems. The 2003 Beijing SARS data are used to validate the effectiveness of the model. In addition, comprehensive numerical experiments are conducted to understand the importance of modeling travel contagion during urban disease outbreaks and develop control strategies for regulating the entry of urban transportation system to reduce the epidemic size. </div><div></div><div>The second part of the dissertation develops a data-driven framework to investigate the disease spreading dynamics at individual level. In particular, the contact network generation algorithm is developed to reproduce individuals' contact pattern based on smart card transaction data of metro systems from three major cities in China. Disease dynamics are connected with contact network structures based on individual based mean field and origin-destination pair based mean field approaches. The results suggest that the vulnerability of contact networks solely depends on the risk exposure of the most dangerous individual, however, the overall degree distribution of the contact network determines the difficulties in controlling the disease from spreading. Moreover, the generation model is proposed to depict how individuals get into contact and their contact duration, based on their travel characteristics. The metro data are used to validate the correctness of the generation model, provide insights on monitoring the risk level of transportation systems, and evaluate possible control strategies to mitigate the impacts due to infectious diseases. </div><div></div><div>Finally, the third part of the dissertation focuses on the role played by information in urban travel, and develops a multiplex network model to investigate the co-evolution of disease dynamics and information dissemination. The model considers that individuals may obtain information on the state of diseases by observing the disease symptoms from the people they met during travel and from centralized information sources such as news agencies and social medias. As a consequence, the multiplex networks model is developed with one layer capturing information percolation and the other layer modeling the disease dynamics, and the dynamics on one layer depends on the dynamics of the other layer. The multiplex network model is found to have three stable states and their corresponding threshold values are analytically derived. In the end, numerical experiments are conducted to investigate the effectiveness of local and global information in reducing the size of disease outbreaks and the synchronization between disease and information dynamics is discussed. </div><div></div>

Sepsis is the most frequent cause of mortality in most intensive care units. Cardiovascular dysfunction is a major complication associated with sepsis, with high mortality rates up to 70%. Currently, there is no effective treatment approach for sepsis. The integrity of the endothelium is fundamental for the homeostasis of the cardiovascular system. Sepsis induces endothelial cell injury which is the key factor for multiple organ failure. The increased expression of adhesion molecules and chemokines in endothelial cell promotes leukocytes infiltration into the tissue. The loss of tight junction proteins and increased permeability of the endothelial cells will provoke tissue hypoxia and subsequent organ failure. Therefore, preservation of endothelial function is a critical approach for improving sepsis-induced outcome. Here, we showed that endothelial specific protein HSPA12B plays a critical role in the preservation of cardiovascular function in polymicrobial sepsis. HSPA12B is the newest member of HSP70 family which predominantly expresses in endothelial cells. We observed that HSPA12B deficiency (HSPA12B-/-) exaggerated polymicrobial sepsis-induced endothelial dysfunction, leading to worse cardiac dysfunction. HSPA12B-/- significantly increases the expression of adhesion molecules, decreases tight junction protein levels and enhances vascular permeability. HSPA12B-/- alsomarkedly promotes the infiltration of inflammatory cells into the myocardium and inflammatory cytokine production. We investigated the cardioprotective mechanisms of HSPA12B in sepsis induced cardiovascular dysfunction. Exosomes play a critical role in intercellular communication. Exosome is a natural vehicle of microRNAs. We found that exosomes isolated from HSPA12B-/- septic mice induced more expression of adhesion molecules in endothelial cells and inflammation in macrophages. Interestingly, the levels of miR-126 in serum exosomes isolated from HSPA12B-/- septic mice were significantly lowers than in WT septic mice. Importantly, delivery of miR-126 carried exosomes significantly improved cardiac function, suppressed the expression of adhesion molecules, reduced immune cell infiltration in the myocardium, and improved vascular permeability in HSPA12B-/- septic mice. The data suggests that HSPA12B is essential for endothelial function in sepsis and that miR-126 containing exosomes plays a critical role in cardiovascular-protective mechanisms of endothelial HSPA12B in polymicrobial sepsis.

Parkinson’s disease (PD) is an incurable, chronically progressive neurodegenerative disease leading to premature invalidity and death. The locomotor disability of PD patients is mainly rooted in the gradual and insidious degeneration of dopaminergic neurons (DA) projecting from the midbrain substantia nigra (SN) to the basal ganglia striatum, a pathological process highlighted microscopically by the formation of insoluble cytosolic protein aggregates, known as Lewy bodies and Lewy neurites. The pathogenic mechanisms leading to PD remain poorly understood, arguably owing to the lack of suitable animal and cellular experimental models of the disease. Therefore, there is an urgent need for developing reliable experimental models that recapitulate the key features of PD. The recent development of induced pluripotent stem cell (iPSC) technology has enabled the generation of patient-specific iPSC and their use to model human diseases, although it is currently unclear whether this approach could be useful to successfully model age-related conditions. Importantly, disease modeling using iPSC largely relies on the existence of efficient protocols for the differentiation of disease-relevant cell types. Here, we first developed an efficient protocol for the differentiation of iPSC to authentic midbrain-specific DA neurons with SN properties by forced expression of LMX1A using a lentivirus-mediated gene delivery system. Next, we generated an iPSC-based cellular model of PD that recapitulates key phenotypic features of PD, such as DA neuron loss and α-synuclein accumulation in DA neurons from PD patients. Overall, our results demonstrate that we have developed a valuable tool for elucidating the pathogenic mechanisms leading to PD, as well as an experimental platform for screening new drugs that may prevent or rescue neurodegeneration in PD. / La malaltia de Parkinson (MP) és una malaltia neurodegenerativa incurable que causa invalidesa i mort prematura. Els pacients de la malaltia de Parkinson presenten alteracions motores degudes a una degeneració gradual de les neurones dopaminèrgiques que projecten des de la substància nigra fins a l’estriat. A nivell microscòpic s’observa la presència d’agregats proteics insolubles en el citosol de les neurones coneguts com cossos o neurites de Lewy. Els mecanismes patològics responsables de la MP no es coneixen bé, possiblement a causa de la manca de models animals i cel•lulars adequats. Per tant, existeix una gran necessitat de desenvolupar models experimentals fiables que recapitulin les característiques bàsiques de la MP. El recent desenvolupament de les cèl•lules mare pluripotents induïdes (iPSC) ha permès la generació de iPSC específiques de pacient i el seu ús per modelar malalties humanes, ara bé, no és clar si aquesta estratègia es pot utilitzar per modelar exitosament malalties d’origen tardà, com ara la MP. És important destacar que el modelatge de malalties utilitzant iPSC, es basa, en gran mesura en l'existència de protocols eficients per a la diferenciació de les iPSC cap al tipus cel•lular rellevant per a la malaltia. Durant aquest període, per primera vegada, s’ha desenvolupat un protocol per a l’eficient diferenciació de les iPSC cap a neurones dopaminèrgiques amb les propietats característiques de neurones dopaminèrgiques nigrostriatals, mitjançant l’expressió forçada de LMX1A utilitzant vectors lentivirals. A continuació, s’ha generat un model cel•lular usant iPSC derivades de pacients de MP que recapitula les principals característiques fenotípiques de la malaltia, com ara la pèrdua de neurones dopaminèrgiques i l'acumulació de α-sinucleïna en les neurones dopaminèrgiques. En general, els nostres resultats demostren que hem desenvolupat una eina valuosa per a l’estudi dels mecanismes patològics que condueixen a la MP, així com una nova plataforma pel descobriment de nous fàrmacs encaminats a prevenir o evitar la neurodegeneració.

Cancer cells are most clearly characterized by their abnormal and uncontrolled cell growth. One of the most notable theories that explains the vast proliferative capacity of tumorigenic cells is the Warburg effect, a significant shift in metabolism wherein cancer cells preferentially fuel cell division using aerobic glycolysis instead of aerobic respiration. This upregulation of glycolytic fermentation in aerobic environments is highly unusual - glycolysis is typically utilized in anaerobic conditions, but nonetheless dominates cancer metabolic activity in spite of the presence of oxygen. Since the discovery the Warburg effect in the 1920s, researchers have struggled to identify whether aerobic glycolysis is a cause or consequence of carcinogenesis. Interestingly, a new theory recently emerged that challenges this widely-accepted metabolic paradigm for cancer. Known as the reverse Warburg effect, this new mechanism shows that in carcinomas such as breast cancer, the Warburg effect occurs not in cancer cells, but rather in tumor-adjacent stromal fibroblasts. These cancer-associated fibroblasts (CAFs) in the greater tumor microenvironment produce lactate - a high-energy metabolite formed as a byproduct of aerobic glycolysis - to fuel aerobic respiration and rapid tumorigenesis in neighboring cancer cells. This emerging theory emphasizes the pivotal role of the tumor microenvironment in determining whether cancer cells undergo aerobic glycolysis or aerobic respiration. Central to this lactate-linked metabolic intersection are two critical enzymes that regulate a cell's metabolic commitment - lactate dehydrogenase (LDH) and pyruvate dehydrogenase complex (PDHc). In order to clarify the mechanisms through which CAFs induce tumorigenesis in breast cancer, we plan to carry out two specific aims: (1) evaluate the enzymatic activity of LDH and PDHc, and (2) compare LDH and PDHc enzyme content. Using co-culture techniques to study the breast cancer tumor microenvironment in vitro, we will compare the enzymatic activity and enzyme content of both MCF7 breast cancer cells and CAFs to identify whether the reverse Warburg effect occurs due to post-translational enzyme activation or increased enzyme synthesis.

Mathematical and computational epidemiological models are important tools in efforts to combat the spread of infectious diseases. The models can be used to predict further progression of an epidemic and for assessing potential countermeasures to control disease spread. In the proposal of models (when data is available), one needs parameter estimation methods. In this thesis, likelihood-less Bayesian inference methods are concerned. The data and the model originate from the spread of a verotoxigenic Escherichia coli in the Swedish cattle population. In using the SISE3 model, which is an extension of the susceptible-infected-susceptible model with added environmental pressure and three age categories, two different methods were employed to give an estimated posterior: Approximate Bayesian Computations and Synthetic Likelihood Markov chain Monte Carlo. The mean values of the resulting posteriors were close to the previously performed point estimates, which gives the conclusion that Bayesian inference on a nation scaled SIS-like network is conceivable.

La modélisation des maladies infectieuses représente un outil important pour évaluer la vulnérabilité d'une population à l'introduction d'un nouveau agent pathogène. La possibilité d’enregistrer les contacts responsables de la propagation des maladies représente à la fois une ressource et un défi pour les modèles épidémiques. En particulier, l'interaction entre la dynamique des maladies et l'évolution dans le temps des structures de contact influence la façon dont les agents pathogènes se propagent, en changeant les conditions qui mènent à une flambée épidémique (seuil épidémique). Jusqu'à maintenant, les chercheurs n'ont caractérisé le seuil épidémique sur des structures de contact qui évoluent dans le temps que dans des contextes spécifiques. En utilisant un formalisme multi-couches, nous calculons analytiquement le seuil épidémique sur un réseau temporel générique. Nous utilisons cette méthode pour évaluer l'impact de la résolution temporelle et la durée du réseau sur l'estimation du seuil. De plus, grâce à cette méthode, nous évaluons la vulnérabilité globale de différents systèmes à l'introduction d'agents pathogènes, et en particulier nous analysons les réseaux de mouvements des bovins. Les données de contact souvent ne sont pas disponible en temps réel, et cela limite notre capacité de prévision. Pour répondre à ça, nous développons une méthodologie numérique pour prédire le risque épidémique ciblé, qui repose uniquement sur les données de contact passées. Notre travail fournit de nouvelles méthodologies pour évaluer et prédire le risque associé à un agent pathogène émergent, à la fois à l'échelle de la population et en ciblant des hôtes spécifiques. / Infectious disease modeling represents a powerful tool for assessing the vulnerability of a population to the introduction of a new infectious pathogen. The increased availability of highly resolved data tracking host interactions is making epidemic models potentially increasingly accurate. Integrating into them all the features emerging from these data, however, still represents a challenge. In particular, the interaction between disease dynamics and the time evolution of contact structures has been shown to impact the way pathogens spread, changing the conditions that lead to the wide-spreading regime, as encoded in epidemic threshold. Up to now researchers have characterized the epidemic threshold on time evolving contact structures only in specific settings. Using a multilayer formalism, we analytically compute the epidemic threshold on a generic temporal network, accounting for several different disease features. We use this methodology to assess the impact of time resolution and network duration on the estimation of the threshold. Then, thanks to it, we assess the global vulnerability of different systems to pathogen introduction, and in particular we analyze the networks of cattle trade movements Data collection strategies often inform us only about past network configurations, and that limits our prediction capabilities. We face this by developing a data-driven methodology for predicting targeted epidemic that relies only past contact data. Our work provides new methodologies for assessing and predicting the risk associated to an emerging pathogen, both at the population scale and targeting specific hosts.

Dengue fever affects over 390 million people annually worldwide and is of particu- lar concern in Southeast Asia where it is one of the leading causes of hospitalization. Modeling trends in dengue occurrence can provide valuable information to Public Health officials, however many challenges arise depending on the data available. In Thailand, reporting of dengue cases is often delayed by more than 6 weeks, and a small fraction of cases may not be reported until over 11 months after they occurred. This study shows that incorporating data on Google Search trends can improve dis- ease predictions in settings with severely underreported data. We compare penalized regression approaches to seasonal baseline models and illustrate that incorporation of search data can improve prediction error. This builds on previous research show- ing that search data and recent surveillance data together can be used to create accurate forecasts for diseases such as influenza and dengue fever. This work shows that even in settings where timely surveillance data is not available, using search data in real-time can produce more accurate short-term forecasts than a seasonal baseline prediction. However, forecast accuracy degrades the further into the future the forecasts go. The relative accuracy of these forecasts compared to a seasonal average forecast varies depending on location. Overall, these data and models can improve short-term public health situational awareness and should be incorporated into larger real-time forecasting efforts.

GM2 gangliosidoses are a family of lysosomal storage disorders that include both Tay-Sachs and Sandhoff diseases. These disorders result from deficiencies in the lysosomal enzyme β-N-acetylhexosaminidase (HexA). Impairment of HexA leads to accumulation of its substrate, GM2 ganglioside, in cells resulting in cellular dysfunction and death. There is currently no treatment for GM2 gangliosidoses. Patients primarily present with neurological dysfunction and degeneration. Here we developed a central nervous system gene therapy through direct injection that leads to long-term survival in the Sandhoff disease mouse model. We deliver an equal mixture of AAVrh8 vectors that encode for the two subunits (α and β) of HexA into the thalami and lateral ventricle. This strategy has also been shown to be safe and effective in treating the cat model of Sandhoff disease. We tested the feasibility and safety of this therapy in non-human primates, which unexpectedly lead to neurotoxicity in the thalami. We hypothesized that toxicity was due to high overexpression of HexA, which dose reduction of vector could not compensate for. In order to maintain AAV dose, and therefore widespread HexA distribution in the brain, six new vector designs were screened for toxicity in nude mice. The top three vectors that showed reduction of HexA expression with low toxicity were chosen and tested for safety in non-human primates. A final formulation was chosen from the primate screen that showed overexpression of HexA with minimal to no toxicity. Therapeutic efficacy studies were performed in Sandhoff disease mice to define the minimum effective dose.

Amyotrophic lateral sclerosis (ALS) is a lethal, progressive neurodegenerative disorder that selectively affects both upper and lower motor neurons, leading to muscle weakness, paralysis and death. Despite recent advances in the identification of genes associated with ALS, the quest for a sensitive biomarker for rapid and accurate diagnosis, prognosis, and treatment response monitoring has not been fulfilled. In this thesis, I report a method of quantifying the integrity of motor neurons in vivo using imaging to record uptake and retrograde transport of intramuscularly injected tetanus toxin fragment C (TTC) into spinal motor neurons. This method tracks and profiles progression of disease (transgenic SOD1G93A and PFN1 ALS mice) and detects subclinical perturbations in net transport, as analyzed in C9orf72 transgenic mice. It also defines a progressive reduction in net transport with aging. To address whether our technique enables drug development, I evaluated therapeutic benefits of (1) gene editing and (2) mutant gene silencing (with RNAi targeting SOD1) in SOD1G93A transgenic mice by characterizing their net axonal transport profiles. I constructed a computational model to evaluate key molecular processes affected in net axonal transport in ALS mouse model. The model allows prediction of key parameters affected in a C9ORF72 BAC transgenic mouse line. Prior immunization with tetanus toxoid does not preclude use of this assay, and it can be used repetitively in the same subject. This assay of net axonal transport offers broad clinical application as a diagnostic tool for motor neuron diseases and as a biomarker for rapid detection of benefit from therapies for transport dysfunction in a range of motor neuron diseases.