This thesis describes the development of a framework for computational modelling of electromechanics in the mouse, with the purpose of being able to integrate cellular and tissue scale observations in the mouse and investigate physiological hypotheses. Specifically, the framework is applied to interpret electromechanical coupling mechanisms and the progression of heart failure in genetically modified mice. Chapter 1 introduces the field of computational biology and provides context for the topics to be investigated. Chapter 2 reviews the biological background and mathematical bases for electromechanical models, as well as their limitations. In Chapter 3, a set of efficient computational methods for coupled cardiac electromechanics was developed. Among these are a modified Newton method combined with a solution predictor which achieves a 98% reduction in computational time for mechanics problems. In Chapter 4, this computational framework is extended to a multiscale electromechanical model of the mouse. This electromechanical model includes our novel cardiac cellular contraction model for mice, which is able to reproduce murine contraction dynamics at body temperature and high pacing frequencies, and provides a novel explanation for the biphasic force-calcium relation seen in cardiac myocytes. Furthermore, our electromechanical model of the left ventricle of the mouse makes novel predictions on the importance of strong velocity-dependent coupling mechanisms in generating a plateau phase of ventricular pressure transients during ejection. In Chapter 5, the framework was applied to investigate the progression of heart failure in genetically modified 'Serca2 knockout' mice, which have a major disruption in mechanisms governing calcium regulation in cardiac myocytes. Our modelling framework was instrumental in showing for the first time the incompatibility between previously measured cellular calcium transients and ventricular ejection. We were then able to integrate new experimental data collected in response to these observations to show the importance of beta-adrenergic stimulation in the progression of heart failure in these knockout mice. Chapter 6 presents the conclusions and discusses possibilities for future work.

The decreasing costs of next generation sequencing technologies and the increasing speeds at which they work have lead to an abundance of 'omic datasets. The need for tools and methods to analyze, annotate, and model these datasets to better understand biological systems is growing. Here we present a novel software pipeline to reconstruct the metabolic model of an organism in silico starting from its genome sequence and a novel compilation of biological databases to better serve the generation of metabolic models. We validate these methods using five Gardnerella vaginalis strains and compare the gene annotation results to NCBI and the FBA results to Model SEED models. We found that our gene annotations were larger and highly similar in terms of function and gene types to the gene annotations downloaded from NCBI. Further, we found that our FBA models required a minimal addition of transport reactions, sources, and escapes indicating that our draft pathway models were very complete. We also found that on average our solutions contained more reactions than the models obtained from Model SEED due to a large amount of baseline reactions and gene products found in ASGARD.

Plenty of gene variants have been associated with disease, indicating widespread genetic heterogeneity, which leaves the molecular basis of complex diseases unclear. However, it is widely postulated that the products of genes whose mutations are implicated in the same disease function together in the same biological pathways and it is the disruption of these pathways that underlies the disease. Such pathways are not well defined and their identification could help elucidate disease mechanisms. To discern molecular pathways of relevance to complex disease, I have inferred functional associations between human genes from diverse data types and assessed these associations with a novel phenotype-based method. I could confirm the hypothesis that dysfunctions of genes associated with each other in terms of functional genomic and proteomic data tend to give rise to the same disease. Examining the functional association between disease-associated gene variants, I have found that genes implicated through de novo sequence variants are biased in their coding sequence length and that longer genes tend to cluster together in gene networks, leading to exaggerated p-values in functional studies. I have controlled for the confounding bias and, testing different data sources, found that an integrated phenotypic-linkage network offers superior power to detect functional associations among genes mutated in the same disease. Applying these methods to clinical phenotypes related to intellectual disability, I have observed an increased predictive potential in identifying genes associated with these phenotypes. I have also performed case–control association analyses of variants from an exome-sequencing study of Parkinson’s disease and tested the functional associations of the mutated genes. I have advanced a framework for the identification of biological pathways disrupted in complex disorders, also demonstrating the suitability of this method to functionally sub-cluster the gene variants underlying a complex disorder, with implications for the understanding of disease mechanisms.

La comparaison inter-espèces de voies métaboliques est une problématique importante en biologie. Actuellement, les connaissances sont générées à partir d'expériences sur un nombre relativement limité d'espèces dites modèles. Mieux connaître une espèce permet de valider ou non une inférence faite à partir de ces données expérimentales et de déterminer si ou dans quelle mesure des résultats obtenus sur une espèce modèle peuvent être transposés à une autre espèce. Cette thèse propose une méthode de comparaison inter-espèces de voies métaboliques. Elle compare chaque étape d'une voie métabolique en exploitant les annotations dans Gene Ontology qui leur sont associées. Ce travail valide l'intérêt des mesures de similarités sémantiques pour interpréter ces annotations, propose d'utiliser conjointement une mesure de particularité sémantique et propose une méthode basée sur des motifs de similarité et de particularité pour interpréter chaque étape de voie métabolique. De nombreuses mesures sémantiques quantifient la similarité entre des produits de gènes en fonction des annotations qu'ils ont en commun. Nous en avons identifié et utilisé une adaptée à la problématique de comparaison inter-espèces. En se focalisant sur la part commune aux produits de gènes comparés, les mesures de similarité sémantiques ignorent les caractéristiques spécifiques d'un seul produit de gène. Or la comparaison inter-espèces de voies métaboliques se doit de quantifier non seulement la similarité des produits de gènes qui interviennent dans celles-ci, mais également leurs particularités. Nous avons développé une mesure de particularité sémantique répondant à cette problématique. Pour chaque étape de voie métabolique, nous calculons un profil composé de sa valeur de similarité et de ses deux valeurs de particularité sémantiques. Il n'est pas possible d'établir formellement que deux produits de gènes sont similaires ou que l'un d'eux a des particularités significatives sans disposer d'un seuil de similarité et d'un seuil de particularité. Jusqu'à présent, ces interprétations se faisaient sur la base d'un seuil implicite ou arbitraire. Pour combler ce manque, nous avons développé une méthode de définition de seuils pour les mesures de similarité et de particularité sémantiques. Nous avons enfin appliqué une mesure de similarité inter-espèces et notre mesure de particularité pour comparer le métabolisme des lipides entre l'Homme, la souris et la poule. Nous avons pu interpréter les résultats à l'aide des seuils que nous avions définis. Chez les trois espèces, des particularités ont pu être observées, y compris au niveau de produits de gènes similaires. Elles concernent notamment des processus biologiques et des composants cellulaires. Les fonctions moléculaires présentent une forte similarité et peu de particularités. Ces résultats sont biologiquement pertinents. / Cross-species comparison of metabolic pathways is an important task in biology. It is a major stake for both human health and agronomy. Currently, knowledge is acquired from some experiments on a relatively low number of species referred to as ``models''. A better understanding of a species determines whether to validate or not an inference made from these experimental data. It also determines whether or to what extent results obtained on model species can be transposed to another species. This thesis proposes a cross-species metabolic pathways comparison method. Our method compares each step of a metabolic pathway using the associated Gene Ontology annotations. This work validates the interest of the semantic similarity measures for interpreting these annotations, proposes to use jointly a semantic particularity measure and proposes a method based on similarity and particularity patterns to interpret each metabolic pathway step. Several gene products are involved throughout a metabolic pathway. They are associated to some annotations in order to describe their biological roles. Based on a shared ontology, these annotations allow to compare data from different species and to take into account several level of abstraction. Several semantic measures quantifying the similarity between gene products from their annotations have been developed previously. We have identified and used a semantic similarity measure appropriate for cross-species comparisons. Because they focus on the common part of the compared gene products, the semantic similarity measures ignore their specific characteristics. Therefore, cross-species metabolic pathways comparison has to quantify not only the similarity of the gene products involved, but also their particularity. We have developed a semantic particularity measure addressing this issue. For each pathway step, we proposed to create a profile combining its semantic similarity and its two semantic particularity values. Concerning the results interpretation, it is not possible to establish formally that two gene products are similar or that one of them have some significant particularities without having a similarity threshold and a particularity threshold. So far, these interpretations were based on an implicit or an arbitrary threshold. To address this gap, we developed a threshold definition method for the semantic similarity and particularity measures. We last applied a cross-species similarity measure and our particularity measure to compare the lipid metabolism between human, mice and chicken. We then interpreted the results using the previously defined thresholds. In all three species, we observed some particularities, including on similar genes. They concerned notably some biological processes and cellular components. The molecular functions present a strong similarity and few particularities. These results are biologically relevant.

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Master of Science in Engineering. Johannesburg, 2015 / This research investigates the e ects of the frequency components of the lightning First Short Stroke (FSS) on the current pathway through human tissues using frequency domain analysis. A Double Exponential Function (DEF) is developed to model the FSS with frequency components in the range 10 Hz 100 kHz. Human tissues are simulated using Finite Element Analysis (FEA) in COMSOL and comprises of two types of models: Single Layer Cylindrical Model (SLCM) and Multi-layered Cylindrical Model (MLCM). The SLCM models 54 human tissues independently and the MLCM models the human leg with ve tissue layers: bone marrow, cortical bone, muscle, blood and fat. Three aspects are analysed: current density, complex impedance and power dissipation. From the SLCM results, aqueous tissues have the lowest impedances and tissue heat dissipation is proportional to tissue impedance. Results from the MLCM show that 85% of the FSS current ows through muscle, 11% ows through blood, 3:5% through fat and the rest through cortical bone and bone marrow. From the results, frequency dependent equivalent circuit models consisting of resistors and capacitors connected in series are proposed. The simulation results are correlated with three main clinical symptoms of lightning injuries: neurological, cardiovascular and external burns. The results of this work are applicable to the analysis of High Voltage (HV) injuries at power frequencies. / MT2017

<div>L-phenylalanine (Phe) is an important amino acid which is the precursor of various plant secondary metabolisms. Its biosynthesis and consumption are governed by different levels of regulatory mechanisms, yet our understanding to them are still far from complete. The plant has evolved a complex regulation over Phe, likely due to the fact that a significant portion of carbon assimilated by photosynthesis is diverted to its downstream products. In particular, lignin as one of them, is among the most abundant polymers in plant secondary cell wall. Studies have unraveled the interconnected metabolism involved in lignin biosynthesis, and a hierarchical gene regulatory network on top of it is also being uncovered by different research groups. These biological processes function together for sufficient lignification to ensure cell wall hydrophobicity and rigidity for plant normal growth. Yet on the other hand, the presence of lignin hinders the efficient saccharification process for biofuel production. Therefore, it is fundamental to understand lignin biosynthesis and its upstream Phe biosynthesis in a systematic way, to guide rational metabolic engineering to either reduce lignin content or manipulate its composition <i>in planta</i>.</div><div> </div><div> Phe biosynthesis was predominantly existed in plastids according to previous studies, and there exists a cytosolic synthetic route as well. Yet how two pathways are metabolically coordinated are largely under-explored. Here I describe a flux analysis using time course datasets from ¹⁵N L-tyrosine (Tyr) isotopic labeling studies to show the contributions from two alternative Phe biosynthetic routes in Petunia flower. The flux split between cytosolic and plastidial routes were sensitive to genetic perturbations to either upstream chorismate mutase within shikimate pathway, or downstream plastidial cationic amino-acid transporter. These results indicate the biological significance of having an alternative biosynthetic route to this important amino acid, so that defects of the plastidial route can be partially compensated to maintain Phe homeostasis.</div><div> </div><div> To understand the metabolic dynamics of the upstream part of lignin biosynthesis, we developed a multicompartmental kinetic model of the general phenylpropanoid metabolism in Arabidopsis basal lignifying stems. The model was parameterized by Markov Chain Monte Carlo sampling, with data from feeding plants with ring labeled [¹³C₆]-Phe. The existence of vacuole storage for both Phe and <i>p</i>-coumarate was supported by an information theoretic approach. Metabolic control analysis with the model suggested the plastidial cationic amino-acid transporter to be the step with the highest flux controlling coefficient for lignin deposition rate. This model provides a deeper understanding of the metabolic connections between Phe biosynthesis and phenylpropanoid metabolism, suggesting the transporter step to be the promising target if one aims to manipulate lignin pathway flux.</div><div> </div><div> Hundreds of gene regulatory interactions between transcription factors and structural genes involved in lignin biosynthesis has been reported with different experimental evidence in model plant Arabidopsis, however, a public database is missing to summarize and present all these findings. In this work, we documented all reported gene regulatory interactions in Arabidopsis lignin biosynthesis, and ended up with a gene regulatory network consisting of 438 interactions between 72 genes. A network is then constructed with linear differential equations, and its parameters were estimated and evaluated with RNA-seq datasets from 13 genetic backgrounds in Arabidopsis basal stems. We combined this network with a kinetic model of lignin biosynthesis starting from Phe and ending with all monolignols participated in lignin polymerization. This hierarchical kinetic model is the first model integrating dynamic information between transcriptional machinery and metabolic network for lignin biosynthesis. We showed that it is able to provide mechanistic explanations for most of experimental findings from different genotypes. It also provides the opportunity to systematically test all possible genetic manipulation strategies targeting to lignification relevant genes to predict the lignin phenotypes <i>in silico</i>.</div>

Background. An enormous accumulation of genomic data has been taking place over the last ten years. This makes the activities of visualization and manual inspection, key steps in trying to understand large datasets containing DNA sequences with millions of letters. This situation has created a gap between data complexity and qualified personnel due to the need of trading between visualization, reduction capacity and exploratory functions, features rarely achieved by existing tools, such as SRA toolkit (https://www.ncbi.nlm.nih.gov/sra/docs/toolkitsoft/), for instance. A novel approach to the problem of genomic analysis and visualization was pursued in this project, by means of STrAtified Interspersed Reduction Structures (STAIRS). Result. Ten weeks of intense work resulted in novel algorithms to compress data, transform it into stairs vectors and align them. Smith–Waterman and Needleman–Wunsch algorithms have been specially modified for this purpose and the application brought about statistical performance and behavioural charts.

As convulsões febris complexas durante a infância representam um fator de risco relevante para o desenvolvimento da epilepsia. Apesar desse fato, as alterações moleculares induzidas por essas crises febris, que tornam o cérebro susceptível ao processo de epileptogênese, ainda são pouco conhecidas. Nesse contexto, a utilização de modelos animais de crises febris induzidas por hipertermia (HS) permite o estudo das alterações moleculares a partir de uma análise temporal desse processo. Assim, neste trabalho foram investigadas as alterações temporais nos perfis de microRNAs e de expressão gênica em explantes da região CA3 hipocampal de ratos Wistar obtidas em quatro intervalos de tempo após o insulto hipertérmico no décimo primeiro dia pós-natal (P11). Os intervalos temporais foram selecionados para avaliar as fases aguda (P12), latente (P30 e P60) e crônica (P120). A análise transcriptômica consistiu na construção de redes de co-expressão gênica, permitindo a identificação de módulos de genes e sua relação com os grupos experimentais e intervalos de tempo selecionados. Os genes também foram caracterizados hierarquicamente, identificando-se genes que conferem robustez às redes de co-expressão gênica (hubs). Além disso, foram avaliados o perfil de expressão diferencial de microRNAs e feita a análise integrada da expressão de microRNAs e expressão gênica dos hubs. Os resultados deste trabalho mostraram que: i) o insulto hipertérmico leva a alterações importantes no desenvolvimento e funcionamento cerebral ii) essas alterações estão associadas a uma assinatura temporal, presumivelmente da epileptogênese à readaptação do cérebro frente ao insulto precipitante inicial; iii) isso envolve um mecanismo de regulação das redes de co-expressão gênica por microRNAs. Esses resultados sugerem que as alterações transcricionais desencadeadas pelo insulto febril podem levar à reprogramação neuronal e ao remodelamento da cromatina, tornando o cérebro susceptível ao processo epiléptico crônico. Como nas epilepsias humanas por insulto febril, o modelo em rato reflete um processo que vai da epileptogênese à cronificação na fase adulta. Como muitos dos casos de epilepsia por insulto febril são refratários a drogas anticonvulsivantes, o entendimento temporal dos mecanismos moleculares envolvidos nesse tipo de epilepsia é relevante para se identificar alvos terapêuticos e desenvolver drogas anti-epileptogênicas / Complex febrile seizures during childhood represent a relevant risk factor for the development of epilepsy. Despite this fact, the molecular alterations induced by febrile seizures that make the brain susceptible to the process of epileptogenesis are still poorly understood. In this context, the animal models of febrile seizures induced by hyperthermia (HS) allow the study of the molecular alterations from a temporal perspective. Thus, we investigated the temporal alterations in the profiles of gene expression and microRNAs in explants of the hippocampal CA3 region of Wistar rats, here obtained at four-time intervals after the hyperthermal insult on the eleventh postnatal day (P11). Time intervals were selected to evaluate the acute (P12), latent (P30 and P60) and chronic (P120) phases. Transcriptomic analysis consisted of constructing gene co-expression networks, allowing the identification of gene modules related to selected time intervals. Genes were also characterized hierarchically identifying those that control the robustness of gene co-expression networks (hubs). In addition, the differential expression profile of microRNA and the integrated analysis of microRNA expression and hub\'s gene expression were evaluated. The results of this work showed that: i) hyperthermic insults lead to important changes in cerebral development and functioning related to febrile seizures; ii) each time interval shows a transcriptomic signature, probably reflecting the process from epileptogenesis to brain readaptation after the initial precipitating insult; iii) this process involves a mechanism of regulation of gene co-expression networks by microRNAs. These results suggest that transcriptional changes triggered by febrile insults may lead to neuronal reprogramming and chromatin remodeling, making the brain susceptible to the chronic epileptic process. Human epilepsy triggered by febrile insults in childhood is related to resistance to antiepileptic drugs and no anti-epileptogenic drug was developed so far. Therefore, a better understanding of the temporal mechanisms involved in the development of chronic epilepsy is mandatory in order to discover new therapeutic targets and, eventually, anti-epileptogenic drugs

Inférer des paramètres épidémiologiques à partir de phylogénies ou de données d'incidence est toujours un enjeu.D'une part, les approches basées sur les données d'incidence donnent souvent des estimations erronées du fait du biais d'échantillonnage important sur ce type de données.D'autre part, les approches utilisant les phylogénies reposent généralement sur des fonctions de vraisemblance exprimées à partir de modèles démographiques relativement simples et peu pertinents au regard des dynamiques épidémiologiques.A notre connaissance, il n'existe aucune méthode d'inférence utilisant les deux types de données, qui se base sur des modèles épidémiologiques.Ce travail de thèse a donc conduit au développement de méthodes de calcul bayésien approché qui ne nécessitent aucune fonction de vraisemblance.Ces approches sont basées sur des simulations à partir de modèles épidémiologiques, des techniques de régression et un grand nombre de statistiques de résumé qui permettent de capturer l'information épidémiologique des phylogénies et des données d'incidence.Nous avons comparé ces nouvelles méthodes de calcul bayésien approché à diverses approches existantes permettant d'inferer des paramètres épidémiologiques à partir de phylogénies ou de données d'incidence et obtenu des résultats tout au moins similaires.Ces approches nous ont ensuite permis d'étudier la dynamique de l'épidémie de virus Ebola de 2013-2016 en Sierra Leone et celle de l'épidémie de VIH-O au Cameroun.Ce travail est un premier pas vers l'application de méthodes sans-vraisemblance à des modèles complexes, de façon à aider les organismes de santé publique à établir des mesures de contrôle plus efficaces. / Inferring epidemiological parameters from phylogenies or incidence data is still challenging.In one hand, approaches based on incidence data give regularly erroneous estimates, because sampling bias is usually important on that type of data.In the other hand, approaches based on phylogenies generally rely on likelihood functions that are expressed from relatively simple demographic models.These demographic models are usually not appropriate to properly describe the epidemiological dynamics.To our knowledge, there is no inference method that uses both types of data and that is based on epidemiological models.This thesis work thus led to the development of approximate Bayesian computation methods, which do not require a likelihood function.These approaches rely on simulations from epidemiological models, regression techniques and a large number of summary statistics, which capture the epidemiological information from phylogenies and incidence data.We compared these new methods of approximate Bayesian computation to diverse existing approaches that infer epidemiological parameters from phylogenies or incidence data, and we obtained at least similar accuracies.These approaches enabled us to study the dynamics of the 2013-2016 Ebola epidemic in Sierra Leone and the dynamics of the HIV-O epidemic in Cameroon.This works is a first step towards the application of likelihood-free approaches to complex epidemiological models in order to help public health organisms to establish more efficient control measures.

The goal of the Encyclopedia of DNA Elements (ENCODE) project has been to characterize all the functional elements of the human genome. These elements include expressed transcripts and genomic regions bound by transcription factors (TFs), occupied by nucleosomes, occupied by nucleosomes with modified histones, or hypersensitive to DNase I cleavage, etc. Chromatin Immunoprecipitation (ChIP-seq) is an experimental technique for detecting TF binding in living cells, and the genomic regions bound by TFs are called ChIP-seq peaks. ENCODE has performed and compiled results from tens of thousands of experiments, including ChIP-seq, DNase, RNA-seq and Hi-C. These efforts have culminated in two web-based resources from our lab—Factorbook and SCREEN—for the exploration of epigenomic data for both human and mouse. Factorbook is a peak-centric resource presenting data such as motif enrichment and histone modification profiles for transcription factor binding sites computed from ENCODE ChIP-seq data. SCREEN provides an encyclopedia of ~2 million regulatory elements, including promoters and enhancers, identified using ENCODE ChIP-seq and DNase data, with an extensive UI for searching and visualization. While we have successfully utilized the thousands of available ENCODE ChIP-seq experiments to build the Encyclopedia and visualizers, we have also struggled with the practical and theoretical inability to assay every possible experiment on every possible biosample under every conceivable biological scenario. We have used machine learning techniques to predict TF binding sites and enhancers location, and demonstrate machine learning is critical to help decipher functional regions of the genome.