Beyond Flatland : exploring graphs in many dimensions

Societies, technologies, economies, ecosystems, organisms, . . . Our world is composed of complex networks—systems with many elements that interact in nontrivial ways. Graphs are natural models of these systems, and scientists have made tremendous progress in developing tools for their analysis. However, research has long focused on relatively simple graph representations and problem specifications, often discarding valuable real-world information in the process. In recent years, the limitations of this approach have become increasingly apparent, but we are just starting to comprehend how more intricate data representations and problem formulations might benefit our understanding of relational phenomena. Against this background, our thesis sets out to explore graphs in five dimensions: descriptivity, multiplicity, complexity, expressivity, and responsibility. Leveraging tools from graph theory, information theory, probability theory, geometry, and topology, we develop methods to (1) descriptively compare individual graphs, (2) characterize similarities and differences between groups of multiple graphs, (3) critically assess the complexity of relational data representations and their associated scientific culture, (4) extract expressive features from and for hypergraphs, and (5) responsibly mitigate the risks induced by graph-structured content recommendations. Thus, our thesis is naturally situated at the intersection of graph mining, graph learning, and network analysis.Gesellschaften, Technologien, Volkswirtschaften, Ökosysteme, Organismen, . . . Unsere Welt besteht aus komplexen Netzwerken—Systemen mit vielen Elementen, die auf nichttriviale Weise interagieren. Graphen sind natürliche Modelle dieser Systeme, und die Wissenschaft hat bei der Entwicklung von Methoden zu ihrer Analyse große Fortschritte gemacht. Allerdings hat sich die Forschung lange auf relativ einfache Graphrepräsentationen und Problemspezifikationen beschränkt, oft unter Vernachlässigung wertvoller Informationen aus der realen Welt. In den vergangenen Jahren sind die Grenzen dieser Herangehensweise zunehmend deutlich geworden, aber wir beginnen gerade erst zu erfassen, wie unser Verständnis relationaler Phänomene von intrikateren Datenrepräsentationen und Problemstellungen profitieren kann. Vor diesem Hintergrund erkundet unsere Dissertation Graphen in fünf Dimensionen: Deskriptivität, Multiplizität, Komplexität, Expressivität, und Verantwortung. Mithilfe von Graphentheorie, Informationstheorie, Wahrscheinlichkeitstheorie, Geometrie und Topologie entwickeln wir Methoden, welche (1) einzelne Graphen deskriptiv vergleichen, (2) Gemeinsamkeiten und Unterschiede zwischen Gruppen multipler Graphen charakterisieren, (3) die Komplexität relationaler Datenrepräsentationen und der mit ihnen verbundenen Wissenschaftskultur kritisch beleuchten, (4) expressive Merkmale von und für Hypergraphen extrahieren, und (5) verantwortungsvoll den Risiken begegnen, welche die Graphstruktur von Inhaltsempfehlungen mit sich bringt. Damit liegt unsere Dissertation naturgemäß an der Schnittstelle zwischen Graph Mining, Graph Learning und Netzwerkanalyse

Multivariate analysis of the immune response upon recent acquisition of Mycobacterium tuberculosis infection

Tuberculosis (TB), caused by the pathogen Mycobacterium tuberculosis (M.tb), is the leading cause of mortality due to an infectious agent worldwide. Based on data from an adolescent cohort study carried out from May 2005 to February 2009, we studied and compared the immune responses of individuals from four cohorts that were defined based on their longitudinal QFT results: the recent QFT converters, the QFT reverters, the persistent QFT positives and negatives. Analysis was based on the integration of different arms of the immune response, including adaptive and “innaptive” responses, measured on the cohorts. COMPASS was used to filter the adaptive dataset and identify bioligically meaningful subsets, while, for the innaptive dataset, we came up with a novel filtering method. Once the datasets were integrated, they were standardized using variance stabilizing (vast) standardization and missing values were imputed using a multiple factor analysis (MFA)-based approach. We first set out to define a set of immune features that changed during recent M.tb infection. This was achieved by employing the kmlShape clustering algorithm to the recent QFT converters. We identified 55 cell subsets to either increase or decrease post-infection. When we assessed how the associations between these changed pre- and post-infection using correlation networks, we found no notable differences. By comparing the recent QFT converters and the persistent QFT positives, a blood-based biomarker to distinguish between recent and established infection, namely ESAT6/CFP10-specific expression of HLA-DR on total Th1 cells, was identified using elastic net (EN) models (average AUROC = 0.87). The discriminatory ability of this variable was confirmed using two tree-based models. Lastly, to assess whether the QFT reverters are a biologically distinct group of individuals, we compared them to the persistent QFT positive and QFT negative individuals using a Projection to Latent Space Discriminant Analysis (PLS-DA) model. The results indicated that reverters appeared more similar to QFT negative individuals rather than QFT positive. Hence, QFT reversion may be associated with clearance of M.tb infection. Immune signatures associated with recent infection could be used to refine end-points of clinical trials testing vaccine efficacy against acquisition of M.tb infection, while immune signatures associated with QFT reversion could be tested as correlates of protection from M.tb infection

Tackling complexity in biological systems: Multi-scale approaches to tuberculosis infection

Tuberculosis is an ancient disease responsible for more than a million deaths per year worldwide, whose complex infection cycle involves dynamical processes that take place at different spatial and temporal scales, from single pathogenic cells to entire hosts' populations. In this thesis we study TB disease at different levels of description from the perspective of complex systems sciences. On the one hand, we use complex networks theory for the analysis of cell interactomes of the causative agent of the disease: the bacillus Mycobacterium tuberculosis. Here, we analyze the gene regulatory network of the bacterium, as well as its network of protein interactions and the way in which it is transformed as a consequence of gene expression adaptation to disparate environments. On the other hand, at the level of human societies, we develop new models for the description of TB spreading on complex populations. First, we develop mathematical models aimed at addressing, from a conceptual perspective, the interplay between complexity of hosts' populations and certain dynamical traits characteristic of TB spreading, like long latency periods and syndemic associations with other diseases. On the other hand, we develop a novel data-driven model for TB spreading with the objective of providing faithful impact evaluations for novel TB vaccines of different types