Thesis (Ph. D.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on January 3, 2008) Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [88]-114).

Thesis (Ph. D.)--Ohio State University, 2008.

Thesis (M.Sc.)--University of Alberta, 2009. / A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Medicine. Title from pdf file main screen (viewed on September 18, 2009). Includes bibliographical references.

Thesis (Ph.D. in Neuroscience) -- University of Colorado, 2005. / Typescript. Includes bibliographical references (leaves 96-111). Free to UCDHSC affiliates. Online version available via ProQuest Digital Dissertations;

Thesis (M.S.)--Ohio University, June, 2005. / Title from PDF t.p. Includes bibliographical references (p. 77-88)

No description available.

Les travaux de cette thèse portent principalement sur le développement d'une méthodologie pour la modélisation de systèmes biologiques. Cette méthodologie, basée sur une modélisation en équations différentielles, intègre aussi bien des méthodes formelles (solveur sur intervalles, solveur de formules STL), qu'analytiques (calcul de stabilité d'état stationnaire) ou numériques (algorithme d'optimisation, analyses statistiques). Elle permet l'intégration de différents types de données, telles la réponse comportementale à une perturbation ou des données quantitatives (demie-vie, concentrations). En collaboration avec une équipe de biologistes, cette méthodologie est appliquée, avec succès, au système de l'homéostasie du fer : nous étudions la réponse intracellulaire du système, via des protéines régulatrices spécifiques (protéines IRP), face à une situation de carence en fer. Un résultat majeur de cette étude est l'amélioration des connaissances sur la concentration de fer intracellulaire nécessaire à la prolifération des cellules : cette concentration est mise en avant par l'étude du modèle, puis est confirmée expérimentalement.Le deuxième volet de ces travaux portent sur le développement d'un outil pour la modélisation de réseaux de gènes avec le formalisme des réseaux de Thomas. Cet outil, développé en ASP (Answer Set Programming), permet l'intégration de différents types de données telles des données sur des mutants ou l'existence de différents états stationnaires. Cet outil permet d'éviter automatiquement l'incohérence en cas de contradiction entre différentes hypothèses sur le système. Il permet également l'inférence de propriétés biologiques telles que l'ordre entre paramètres cinétiques. / The major part of this PhD consists in the creation of a methodology to model biological systems. This methodology considers models based on differential equations, and uses formal methods (interval solver, verification of STL formula), analytical methods (study of stability) and numerical methods (optimization algorithm, statistical analysis). Moreover, many kind of data, like behavioral response to perturbation, or quantitative data (metabolite half-life and concentration) can be incorporated. In collaboration with a biologist team, this methodology is successfully applied to the iron homeostasis network : we study the response of the system to an iron depletion, at the intracellular level, based on specific regulatory proteins (IRP proteins). A major output of this study is insight into the level of iron cells need to proliferate : this concentration is pointed out by the study of the model, and is experimentally validated.The second part of the PhD is the creation of a tool to model genetic regulatory networks, using Thomas' formalism. This tool, developed using ASP (Answer Set Programming) programming language, can integrate many kind of data, like mutation data, or the existence of many steady states. It automatically avoids inconsistency in case of contradiction between different hypotheses. It also infers biological properties such as relationships between kinetic parameters.

Alzheimer’s disease (AD) is the most common cause of dementia, affecting almost 40 million people worldwide, and it is predicted that this number will rise to nearly 150 million by 2050. It results not only in enormous distress for affected individuals and carers but also a substantial economic burden on society. Although more than 100 years have passed since its discovery, no cure for AD exists, despite enormous efforts in basic and clinical research over the past few decades, due to limited understanding of its underlying mechanisms. Neurodegenerative disorders, of which AD is an example, are highly complex disorders characterized by extensive neuronal dysfunction associated with the misfolding and aggregation of a specific set of proteins, including amyloid plaques and neurofibrillary tangles in AD. One promising avenue for progress in the field is to improve our understanding of the mechanisms by which cellular dysfunction arises from the initial protein aggregation events. The studies described in the thesis are based on the recent finding that a large number of proteins are inherently supersaturated, being expressed at concentrations higher than their solubilities, and constituting a metastable subproteome potentially susceptible to aggregation. These studies illustrate the dependence of aggregation prone metastable proteins on the cellular degradation machineries. They also study the role of metastable proteins and their homeostasis complement in the vulnerability of various body and brain tissues to protein aggregation diseases. Using extensive sequencing data and network based systems biology approaches, they elucidate how fundamental physicochemical properties of an individual or group of proteins relate to their biological function or dysfunction.

Maintaining the intrinsic excitability of neurons is crucial for stable brain activity. This can be achieved by the homeostatic regulation of membrane ion channel conductances, although it is not well understood how these processes influence broader aspects of neuron and network function. One of the many mechanisms which contribute towards this task is the modulation of potassium channel conductances by activity-dependent nitric oxide signalling. Here, we first investigate this mechanism in a conductance-based neuron model. By fitting the model to experimental data we find that nitric oxide signalling improves synaptic transmission fidelity at high firing rates, but that there is an increase in the metabolic cost of action potentials associated with this improvement. Although the improvement in function had been observed previously in experiment, the metabolic constraint was unknown. This additional constraint provides a plausible explanation for the selective activation of nitric oxide signalling only at high firing rates. In addition to mediating homeostatic control of intrinsic excitability, nitric oxide can diffuse freely across cell membranes, providing a unique mechanism for neurons to communicate within a network, independent of synaptic connectivity. We next conduct a theoretical investigation of the distinguishing roles of diffusive homeostasis mediated by nitric oxide in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis robustly maintain stable activity. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that diffusive homeostasis interferes less than non-diffusive homeostasis in the synaptic weight dynamics of networks undergoing Hebbian plasticity. Overall, these results suggest a novel homeostatic mechanism for maintaining stable network activity while simultaneously minimising metabolic cost and conserving network functionality.