Navigability and synchronization in complex networks: a computational approach

Les xarxes complexes han demostrat ser una eina molt valuosa per estudiar sistemes reals, en part, gràcies a la creixent capacitat de computació. En aquesta tesi abordem computacionalment diversos problemes dividits en dos blocs. El primer bloc està motivat pels problemes que planteja la ràpida evolució de la Internet. D’una banda, el creixement exponencial de la xarxa està comprometent la seva escalabilitat per les dependències a les taules d’enrutament globals. Al Capítol 4 proposem un esquema d’enrutament descentralitzat que fa servir la projecció TSVD de l’estructura mesoscòpica de la xarxa com a mapa. Els resultats mostren que fent servir informació local podem guiar amb èxit en l’enrutament. Al Capítol 3 també avaluem la fiabilitat d’aquesta projecció davant el creixement de la xarxa. Els resultats indiquen que aquest mapa és robust i no necessita actualitzacions contínues. D’altra banda, la creixent demanda d’ample de banda és un factor potencial per produir congestió. Al Capítol 5 estenem un esquema d’enrutament dinàmic en el context de les xarxes multiplex, i l’analitzem amb xarxes sintètiques amb diferents assortativitats d’acoblament. Els resultats mostren que tenir en compte el volum de trànsit en l’enrutament retarda l’inici de la congestió. Tot i això, la distribució uniforme del trànsit produeix una transició de fase abrupta. Amb tot, l’acoblament assortatiu es presenta com la millor opció per a dissenys de xarxes òptimes. El segon bloc ve motivat per l’actual crisi financera mundial. Al Capítol 6 proposem estudiar la propagació de les crisis econòmiques utilitzant un model simple de xarxa formada per oscil·ladors integrate-and-fire, i caracteritzar la seva sincronització durant l’evolució de la xarxa de comerç. Els resultats mostren l’aparició d’un procés de globalització que dilueix les fronteres topològiques i accelera la propagació de les crisis financeres.Las redes complejas han demostrado ser una herramienta muy valiosa para estudiar sistemas reales, en parte, gracias a la creciente capacidad de computación. En esta tesis abordamos computacionalmente varios problemas divididos en dos bloques. El primer bloque está motivado por los problemas que plantea la rápida evolución de Internet. Por un lado, el crecimiento exponencial de la red está comprometiendo su escalabilidad por las dependencias a las tablas de enrutado globales. En el Capítulo 4 proponemos un esquema de enrutamiento descentralizado que utiliza la proyección TSVD de la estructura mesoscópica de la red como mapa. Los resultados muestran que utilizando información local podemos guiar con éxito el enrutado. En el Calítulo 3 también evaluamos la fiabilidad de esta proyección bajo cambios en la topología de la red. Los resultados indican que este mapa es robusto y no necesita actualizaciones continuas. Por otra parte, la creciente demanda de ancho de banda es un factor potencial de congestión. En el Capítulo 5 extendemos un esquema de enrutamiento dinámico en el marco de las redes multiplex, y lo analizamos en redes sintéticas con distintas asortatividades de acoplamiento. Los resultados muestran que tener en cuenta el volumen de tráfico en el enrutado retrasa la congestión. Sin embargo, la distribución uniforme del tráfico produce una transición de fase abrupta. Además, el acoplamiento asortativo se presenta como la mejor opción para diseños de redes óptimas. El segundo bloque viene motivado por la actual crisis financiera mundial. En el Capítulo 6 proponemos estudiar la propagación de las crisis económicas utilizando un modelo simple de red formada por osciladores integrate-and-fire, y caracterizar su sincronización durante la evolución de la red de comercio. Los resultados muestran la aparición de un proceso de globalización que diluye las fronteras topológicas y acelera la propagación de las crisis financieras.Complex networks are a powerful tool to study many real systems, partly thanks to the increasing capacity of computational resources. In this dissertation we address computationally a broad scope of problems that are framed in two parts. The first part is motivated by the issues posed by the rapid evolution of the Internet. On one side, the exponential growth of the network is compromising its scalability due to dependencies on global routing tables. In Chapter 4 we propose a decentralized routing scheme that exploits the TSVD projection of the mesoscopic structure of the network as a map. The results show that, using only local information, we can achieve good success rates in the routing process. Additionally, Chapter 3 evaluates the reliability of this projection when network topology changes. The results indicate that this map is very robust and does not need continual updates. On the other side, the increasing bandwidth demand is a potential trigger for congestion episodes. In Chapter 5 we extend a dynamic traffic-aware routing scheme to the context of multiplex networks, and we conduct the analysis on synthetic networks with different coupling assortativity. The results show that considering the traffic load in the transmission process delays the congestion onset. However, the uniform distribution of traffic produces an abrupt phase transition from free-flow to congested state. Withal, assortative coupling is depicted as the best consideration for optimal network designs. The second part is motivated by the current global financial crises. Chapter 6 presents a study on the spreading of economic crises using a simple model of networked integrate-and-fire oscillators and we characterize synchronization process on the evolving trade network. The results show the emergence of a globalization process that dilutes the topological borders and accelerates the spreading of financial crashes

Energy balance of triathletes during an ultra-endurance event.

UNLABELLED: The nutritional strategy during an ultra-endurance triathlon (UET) is one of the main concerns of athletes competing in such events. The purpose of this study is to provide a proper characterization of the energy and fluid intake during real competition in male triathletes during a complete UET and to estimate the energy expenditure (EE) and the fluid balance through the race. METHODS: Eleven triathletes performed a UET. All food and drinks ingested during the race were weighed and recorded in order to assess the energy intake (EI) during the race. The EE was estimated from heart rate (HR) recordings during the race, using the individual HR-oxygen uptake (Vo2) regressions developed from three incremental tests on the 50-m swimming pool, cycle ergometer, and running treadmill. Additionally, body mass (BM), total body water (TBW) and intracellular (ICW) and extracellular water (ECW) were assessed before and after the race using a multifrequency bioimpedance device (BIA). RESULTS: Mean competition time and HR was 755 ± 69 min and 137 ± 6 beats/min, respectively. Mean EI was 3643 ± 1219 kcal and the estimated EE was 11,009 ± 664 kcal. Consequently, athletes showed an energy deficit of 7365 ± 1286 kcal (66.9% ± 11.7%). BM decreased significantly after the race and significant losses of TBW were found. Such losses were more related to a reduction of extracellular fluids than intracellular fluids. CONCLUSIONS: Our results confirm the high energy demands of UET races, which are not compensated by nutrient and fluid intake, resulting in a large energy deficit

EpiGraphDB: a database and data mining platform for health data science

Motivation: The wealth of data resources on human phenotypes, risk factors, molecular traits and therapeutic interventions presents new opportunities for population health sciences. These opportunities are paralleled by a growing need for data integration, curation and mining to increase research efficiency, reduce mis-inference and ensure reproducible research. Results: We developed EpiGraphDB (https://epigraphdb.org/), a graph database containing an array of different biomedical and epidemiological relationships and an analytical platform to support their use in human population health data science. In addition, we present three case studies that illustrate the value of this platform. The first uses EpiGraphDB to evaluate potential pleiotropic relationships, addressing mis-inference in systematic causal analysis. In the second case study, we illustrate how protein-protein interaction data offer opportunities to identify new drug targets. The final case study integrates causal inference using Mendelian randomization with relationships mined from the biomedical literature to 'triangulate' evidence from different sources

Changes in the gene expression profile during spontaneous migraine attacks

Migraine attacks are delimited, allowing investigation of changes during and outside attack. Gene expression fluctuates according to environmental and endogenous events and therefore, we hypothesized that changes in RNA expression during and outside a spontaneous migraine attack exist which are specific to migraine. Twenty-seven migraine patients were assessed during a spontaneous migraine attack, including headache characteristics and treatment effect. Blood samples were taken during attack, two hours after treatment, on a headache-free day and after a cold pressor test. RNA-Sequencing, genotyping, and steroid profiling were performed. RNA-Sequences were analyzed at gene level (differential expression analysis) and at network level, and genomic and transcriptomic data were integrated. We found 29 differentially expressed genes between ‘attack’ and ‘after treatment’, after subtracting non-migraine specific genes, that were functioning in fatty acid oxidation, signaling pathways and immune-related pathways. Network analysis revealed mechanisms affected by changes in gene interactions, e.g. ‘ion transmembrane transport’. Integration of genomic and transcriptomic data revealed pathways related to sumatriptan treatment, i.e. ‘5HT1 type receptor mediated signaling pathway’. In conclusion, we uniquely investigated intra-individual changes in gene expression during a migraine attack. We revealed both genes and pathways potentially involved in the pathophysiology of migraine and/or migraine treatment.publishedVersio

A multi-tissue atlas of regulatory variants in cattle:Cattle Genotype-Tissue Expression Atlas

Characterization of genetic regulatory variants acting on the livestock gene expression is essential for interpreting the molecular mechanisms underlying traits of economic value and for increasing the rate of genetic gain through artificial selection. Here we build a Cattle Genotype-Tissue Expression atlas (CattleGTEx) as part of the pilot phase of Farm animal GTEx (FarmGTEx) project for the research community based on publicly available 7,180 RNA-Seq samples. We describe the transcriptomic landscape of over 100 tissues/cell types and report hundreds of thousands of genetic associations with gene expression and alternative splicing for 23 distinct tissues. We evaluate the tissue-sharing patterns of these genetic regulatory effects, and functionally annotate them using multi-omics data. Finally, we link gene expression in different tissues to 43 economically important traits using both transcriptome-wide association and colocalization analyses to decipher the molecular regulatory mechanisms underpinning such agronomic traits in cattle

Model-based clustering of multi-tissue gene expression data

Motivation: Recently, it has become feasible to generate large-scale, multi-tissue gene expression data, where expression profiles are obtained from multiple tissues or organs sampled from dozens to hundreds of individuals. When traditional clustering methods are applied to this type of data, important information is lost, because they either require all tissues to be analyzed independently, ignoring dependencies and similarities between tissues, or to merge tissues in a single, monolithic dataset, ignoring individual characteristics of tissues.Results: We developed a Bayesian model-based multi-tissue clustering algorithm, revamp, which can incorporate prior information on physiological tissue similarity, and which results in a set of clusters, each consisting of a core set of genes conserved across tissues as well as differential sets of genes specific to one or more subsets of tissues. Using data from seven vascular and metabolic tissues from over 100 individuals in the STockholm Atherosclerosis Gene Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by revamp are more enriched for tissue-dependent protein-protein interactions compared to alternative approaches. We further demonstrate that revamp results in easily interpretable multi-tissue gene expression associations to key coronary artery disease processes and clinical phenotypes in the STAGE individuals

Родники ирбитские. 2021. № 67

Module network inference is a statistical method to reconstruct gene regulatory networks, which uses probabilistic graphical models to learn modules of coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.Comment: Minor revision; 19 pages, 5 figures; chapter for a forthcoming book on gene regulatory network inferenc