Gene Regulatory Networks: Modeling, Intervention and Context

abstract: Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress in increasingly accurate and thorough high-throughput measurement technologies has provided a deluge of data from which we may attempt to infer a representation of the true genetic regulatory system. A gene regulatory network model, if accurate enough, may allow us to perform hypothesis testing in the form of computational experiments. Of great importance to modeling accuracy is the acknowledgment of biological contexts within the models -- i.e. recognizing the heterogeneous nature of the true biological system and the data it generates. This marriage of engineering, mathematics and computer science with systems biology creates a cycle of progress between computer simulation and lab experimentation, rapidly translating interventions and treatments for patients from the bench to the bedside. This dissertation will first discuss the landscape for modeling the biological system, explore the identification of targets for intervention in Boolean network models of biological interactions, and explore context specificity both in new graphical depictions of models embodying context-specific genomic regulation and in novel analysis approaches designed to reveal embedded contextual information. Overall, the dissertation will explore a spectrum of biological modeling with a goal towards therapeutic intervention, with both formal and informal notions of biological context, in such a way that will enable future work to have an even greater impact in terms of direct patient benefit on an individualized level.Dissertation/ThesisPh.D. Computer Science 201

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

DATA-DRIVEN BAYESIAN APPROACH TO THE ANALYSIS OF CELL SIGNALLING NETWORKS IN SYNERGISTIC LIGAND-INDUCED NEURITE OUTGROWTH IN PC12 CELLS

Ph.DDOCTOR OF PHILOSOPH

Statistical model identification : dynamical processes and large-scale networks in systems biology

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014von Robert Johann Flassi

Bayesian belief networks for dementia diagnosis and other applications: a comparison of hand-crafting and construction using a novel data driven technique

The Bayesian network (BN) formalism is a powerful representation for encoding domains characterised by uncertainty. However, before it can be used it must first be constructed, which is a major challenge for any real-life problem. There are two broad approaches, namely the hand-crafted approach, which relies on a human expert, and the data-driven approach, which relies on data. The former approach is useful, however issues such as human bias can introduce errors into the model. We have conducted a literature review of the expert-driven approach, and we have cherry-picked a number of common methods, and engineered a framework to assist non-BN experts with expert-driven construction of BNs. The latter construction approach uses algorithms to construct the model from a data set. However, construction from data is provably NP-hard. To solve this problem, approximate, heuristic algorithms have been proposed; in particular, algorithms that assume an order between the nodes, therefore reducing the search space. However, traditionally, this approach relies on an expert providing the order among the variables --- an expert may not always be available, or may be unable to provide the order. Nevertheless, if a good order is available, these order-based algorithms have demonstrated good performance. More recent approaches attempt to ``learn'' a good order then use the order-based algorithm to discover the structure. To eliminate the need for order information during construction, we propose a search in the entire space of Bayesian network structures --- we present a novel approach for carrying out this task, and we demonstrate its performance against existing algorithms that search in the entire space and the space of orders. Finally, we employ the hand-crafting framework to construct models for the task of diagnosis in a ``real-life'' medical domain, dementia diagnosis. We collect real dementia data from clinical practice, and we apply the data-driven algorithms developed to assess the concordance between the reference models developed by hand and the models derived from real clinical data

Integrative Modeling of Transcriptional Regulation in Response to Autoimmune Desease Therapies

Die rheumatoide Arthritis (RA) und die Multiple Sklerose (MS) werden allgemein als Autoimmunkrankheiten eingestuft. Zur Behandlung dieser Krankheiten werden immunmodulatorische Medikamente eingesetzt, etwa TNF-alpha-Blocker (z.B. Etanercept) im Falle der RA und IFN-beta-Präparate (z.B. Betaferon und Avonex) im Falle der MS. Bis heute sind die molekularen Mechanismen dieser Therapien weitestgehend unbekannt. Zudem ist ihre Wirksamkeit und Verträglichkeit bei einigen Patienten unzureichend. In dieser Arbeit wurde die transkriptionelle Antwort im Blut von Patienten auf jede dieser drei Therapien untersucht, um die Wirkungsweise dieser Medikamente besser zu verstehen. Dabei wurden Methoden der Netzwerkinferenz eingesetzt, mit dem Ziel, die genregulatorischen Netzwerke (GRNs) der in ihrer Expression veränderten Gene zu rekonstruieren. Ausgangspunkt dieser Analysen war jeweils ein Genexpressions- Datensatz. Daraus wurden zunächst Gene gefiltert, die nach Therapiebeginn hoch- oder herunterreguliert sind. Anschließend wurden die genregulatorischen Regionen dieser Gene auf Transkriptionsfaktor-Bindestellen (TFBS) analysiert. Um schließlich GRN-Modelle abzuleiten, wurde ein neuer Netzwerkinferenz-Algorithmus (TILAR) verwendet. TILAR unterscheidet zwischen Genen und TF und beschreibt die regulatorischen Effekte zwischen diesen durch ein lineares Gleichungssystem. TILAR erlaubt dabei Vorwissen über Gen-TF- und TF-Gen-Interaktionen einzubeziehen. Im Ergebnis wurden komplexe Netzwerkstrukturen rekonstruiert, welche die regulatorischen Beziehungen zwischen den Genen beschreiben, die im Verlauf der Therapien differentiell exprimiert sind. Für die Etanercept-Therapie wurde ein Teilnetz gefunden, das Gene enthält, die niedrigere Expressionslevel bei RA-Patienten zeigen, die sehr gut auf das Medikament ansprechen. Die Analyse von GRNs kann somit zu einem besseren Verständnis Therapie-assoziierter Prozesse beitragen und transkriptionelle Unterschiede zwischen Patienten aufzeigen