DEVELOPMENT OF A COMPUTATIONAL RESOURCE FOR PERSONALIZED DIETARY RECOMMENDATIONS

There is a global increase in the incidence of non-communicable diseases associated with unhealthy food intakes. Conditions such as diabetes, heart disease, high blood pressure, and strokes represent a high societal impact and an economic burden for health-care systems around the world. To understand these diseases, one needs to account the several factors that influence how the human body processes food, some of which are determined by the genome and patterns of gene expression that translate to the ability - or lack of - to degrade and absorb certain nutrients. Other factors, like the gut microbiota, are more volatile because its composition is highly moldable by diet and lifestyle. Multi-omics technologies can support the comprehensive collection of dietary intake data and monitoring of the health status of individuals. Also, a correct analysis of this data could lead to new insights about the complex processes involved in the digestion of dietary components and their involvement in the prevention or the appearance of health problems, but its integration and interpretation are still problematic. Thus, in this thesis, we propose the utilization of Constraint-Based Reconstruction and Analysis (COBRA) methods as a framework for the integration of this complex data. To achieve this goal, we have created a knowledge-base, the Virtual Metabolic Human (VMH), that combines information from large-scale models of metabolism from the human organism and typical gut microbes, with food composition information, and a disease compendium. VMH’s unique combination of resources leverages the exploration of metabolic pathways from different organisms, the inclusion of dietary information into in-silico experiments through its own diet designer tool, visualization and analysis of experimental and simulation data, and exploring disease mechanisms and potential treatment strategies. VMH is a step forward in providing the necessary tools to investigate the mechanisms behind the influence of diet in health and disease. Tools such as the diet designer can be used as a basis for diet optimization by predicting combinations of foods that can contribute to specific metabolic outcomes, which has the potential to be integrated and translated into treatment development and dietary recommendations in the foreseeable future

Genetic networks of antibacterial responses of eukaryotic cells. Bioinformatics analysis and modeling

This work describes the development of new methods to construction of promoter models as one of necessary steps of regulatory networks construction. Identification of characteristic promoter features shows the role of specific transcription factors (TFs) in triggering the response, which in turn sheds light on the signaling pathways activating these TFs. Treating reported results of microarray analyses together with other available information about the genes expressed in different cellular systems under consideration, we search for distinguishing features of the promoters of coexpressed genes. The application of such promoter models enables to identify additional candidate genes belonging to the same regulatory network. Four novel approaches are presented in this work: (i) subtractive approach to matrix generation; (ii) distance distribution approach; (iii) "seed" sets approach; (iv) complementary pairs approach. These approaches help to solve serious problems in promoter model construction such as the doubtful reliability of positive training sets ("seed" sets approach) and lack of knowledge about the exact signaling pathways triggering the gene expression (complementary pairs approach); the subtractive approach to matrix generation allows to refine positional weight matrices (PWM) for heterogeneous sets of binding sites, thus to improve the PWM search for single TFBS. A significant improvement of the specificity of promoter analysis has been achieved by applying statistical methods for characterizing TFBS combinations at over-represented distances rather than the mere identification of single potential TFBS (distance distributions approach). The newly developed methods were applied to the description of four defensive eukaryotic systems in terms of transcription regulation. The obtained models enabled us to gain better insights into the pathways of the corresponding signaling networks.Diese Arbeit beschreibt die Entwicklung mehrerer neuer Methoden zur Konstruktion von Promotormodellen als einen der notwendigen Schritte zur Konstruktion regulatorischer Netzwerke. Die Identifizierung charakteristischer Eigenschaften von Promotoren zeigt die Rolle bestimmter Transkriptionsfaktoren (TF) beim Auslösen spezifischer Antworten auf, was wiederum Aufschluss über die Signalwege zur Aktivierung dieser TF gibt. Durch Verarbeitung von Ergebnissen aus Microarray-Analysen zusammen mit weiteren verfügbaren Informationen über die in den betrachteten zellulären Systemen exprimierten Gene suchen wir nach kennzeichnenden Eigenschaften koregulierter Promotoren. Die Applikation solcher Promotermodelle ermöglicht die Identifizierung zusätzlicher Kandidatengene, die demselben regulatorischen Netzwerk angehören. Vier neue Ansätze werden in dieser Arbeit präsentiert: (i) der subtraktive Ansatz zur Matrixerzeugung; (ii) der Distanzverteilungsansatz; (iii) der "seed"-Set-Ansatz; (iv) der Ansatz komplementärer Paare. Diese Ansätze helfen, beträchtliche Probleme der Promotormodellkonstruktion zu lösen, wie die zweifelhafte Zuverlässigkeit positiver Trainingsets ("seed"-Set-Ansatz) und der Mangel an Wissen über die präzisen Signalwege, die bestimmte Genexpressionsereignisse auslösen (Ansatz komplementärer Paare). Der subtraktive Ansatz zur Matrixerzeugung erlaubt, Positionsgewichtungsmatrizen (PWM) für heterogene Sets von Bindungsstellen zu verfeinern und dadurch die PWM-Suche für einzelne TFBSs zur verbessern. Eine signifikante Verbesserung der Spezifität der Promotoranalyse wurde durch die Anwendung statistischer Methoden zur Charakterisierung von TFBS-Kombinationen in überrepräsentierten Distanzen anstelle der bloßen Identifizierung einzelner potentieller TFBSs erreicht. Die neuentwickelten Methoden wurden zur Beschreibung von vier eukaryotischen Abwehrsystemen verwendet. Die erhaltenen Modelle eröffneten tiefergehende Einsichten in die Pfade der zugehörigen Signalnetzwerke

Quantification and Simulation of Liquid Chromatography-Mass Spectrometry Data

Computational mass spectrometry is a fast evolving field that has attracted increased attention over the last couple of years. The performance of software solutions determines the success of analysis to a great extent. New algorithms are required to reflect new experimental procedures and deal with new instrument generations. One essential component of algorithm development is the validation (as well as comparison) of software on a broad range of data sets. This requires a gold standard (or so-called ground truth), which is usually obtained by manual annotation of a real data set. Comprehensive manually annotated public data sets for mass spectrometry data are labor-intensive to produce and their quality strongly depends on the skill of the human expert. Some parts of the data may even be impossible to annotate due to high levels of noise or other ambiguities. Furthermore, manually annotated data is usually not available for all steps in a typical computational analysis pipeline. We thus developed the most comprehensive simulation software to date, which allows to generate multiple levels of ground truth and features a plethora of settings to reflect experimental conditions and instrument settings. The simulator is used to generate several distinct types of data. The data are subsequently employed to evaluate existing algorithms. Additionally, we employ simulation to determine the influence of instrument attributes and sample complexity on the ability of algorithms to recover information. The results give valuable hints on how to optimize experimental setups. Furthermore, this thesis introduces two quantitative approaches, namely a decharging algorithm based on integer linear programming and a new workflow for identification of differentially expressed proteins for a large in vitro study on toxic compounds. Decharging infers the uncharged mass of a peptide (or protein) by clustering all its charge variants. The latter occur frequently under certain experimental conditions. We employ simulation to show that decharging is robust against missing values even for high complexity data and that the algorithm outperforms other solutions in terms of mass accuracy and run time on real data. The last part of this thesis deals with a new state-of-the-art workflow for protein quantification based on isobaric tags for relative and absolute quantitation (iTRAQ). We devise a new approach to isotope correction, propose an experimental design, introduce new metrics of iTRAQ data quality, and confirm putative properties of iTRAQ data using a novel approach. All tools developed as part of this thesis are implemented in OpenMS, a C++ library for computational mass spectrometry

Beyond Enlightenment: The Evolution of Agency and the Modularity of the Mind in a Post-Darwinian World

Working out of the social and philosophical revolutions from the Enlightenment, contemporary action theory has unwittingly inherited several Cartesian ideas regarding the human mind: that it is unified, rational, and transparent. As a result, we have for too long conceived of action as intimately bound up with reason such that to act at all is to act for a reason, leaving us with theoretical difficulties in accounting for the behavior of non-human animals as well as irrational behavior in human beings. But rather than propose that such difficulties can be resolved by retreating to a pre-Enlightenment view of human nature, the solution is to make the philosophical turn and embrace the insights that have been secured by Charles Darwin. It is a post-Darwinian evolutionary worldview that can shed some new light on these traditional problems. Two such innovations from the theory of evolution have been evolutionary explanations, which attempt to understand the functions of organisms as having developed in response to environmental pressures, and modular theory, which views organisms as composed of parts with highly specialized functions. Taking these evolutionary ideas together along with the assumption of biological continuity—that there is a developmental history shared by living organisms—we can begin to conceive of more robust theories of action, mind, and human nature. Contrary to Enlightenment conceptions, reason emerges as just one mental process alongside many, the mind appears anything but Cartesian, and agency begins far earlier along the spectrum of life than we have been supposing