Oscillations lie at the core of many biological processes, from the cell cycle, to circadian oscillations and developmental processes. They are essential to enable organisms to adapt to varying conditions in environmental cycles, from day/night to seasonal. Transcriptional regulatory networks are one of the mechanisms behind these biological oscillations. One of the main problems of computational systems biology is elucidating the interaction between biological components. A common mathematical abstraction is to represent these interactions as networks whose nodes are the reactive species and the interactions are edges. There is abundant literature dealing with the reconstruction of the network structure from steady-state gene expression measurements; still, there are lots of advancements to be made because of the complex nature of biological systems. Experimental design is another obstacle to overcome; we wish to perform experiments that help us best define the network structure according to our current knowledge of the system. In the first chapters of this thesis we will focus on reconstructing the network structure of biological oscillators by explicitly leveraging the cyclical nature of the transcriptional signals. We present a method for reconstructing network interactions tailored to this special but important class of genetic circuits. The method is based on projecting the signal onto a set of oscillatory basis functions. We build a Bayesian hierarchical model within a frequency domain linear model in order to enforce sparsity and incorporate prior knowledge about the network structure. Experiments on real and simulated data show that the method can lead to substantial improvements over competing approaches if the oscillatory assumption is met, and remains competitive also in cases it is not. Having defined a model for gene expression in oscillatory systems, we also consider the problem of designing informative experiments for elucidating the dynamics and better identify the model. We demonstrate our approach on a benchmark scenario in plant biology, the circadian clock network of Arabidopsis thaliana, and discuss the different value of three types of commonly used experiments in terms of aiding the reconstruction of the network. Finally we provide the architecture and design of a software implementation to plug in statistical methods of gene expression inference and network reconstruction into a biological data integration platform.

Malaria is a devastating disease which affects almost half of the world’s population. Since the description of the malaria genome sequence, various aspects of the parasite have been studied, including drug resistance mechanisms, epidemiology and surveillance systems. Alarmingly, very little is known about the basic biological processes such as the regulation of the expression of parasite genes. The parasite, Plasmodium falciparum, has developed highly specialized methods of regulating the transcription of genes, starting at the regulation of genes controlling basic cellular processes such as protein synthesis and erythrocyte invasion, followed by the transcriptional regulation of more specialized genes, such as those aiding in immune evasion and pathogenesis. The description of the P. falciparum transcriptome by Bozdech et al. in 2003 revealed a complex, just-in-time and tightly regulated transcription profile of P. falciparum genes. This suggests that the most probable Achilles heel for Plasmodium may be its unique mechanisms of regulating gene expression. Various cis- and trans-regulatory sequences have been identified in P. falciparum, along with possible DNA (and RNA) binding proteins. The first part of this research focussed on transcriptional regulatory mechanisms in which an in silico search identified cis-regulatory sequences in the 5’ untranslated region of the antigenically variant var gene family. Electrophoretic mobility shift assays (EMSA) were used to identify protein binding partners of these sequences, which could ultimately act as transcription factors in regulating the expression of this essential gene family. The second part of the research investigated the involvement of post-transcriptional regulatory mechanisms in the polyamine biosynthetic pathway of P. falciparum. Polyamines have been proven to be crucial for the parasite’s development and therefore, an RNA interference knock-down strategy was used to verify the importance of the polyamine biosynthetic enzymes S-Adenosylmethionine decarboxylase (AdoMetDC), Ornithine decarboxylase (ODC) and Spermidine synthase. It is clear that various mechanisms for gene regulation are used by the parasite and that this is critical for the survival of this organism. The results of this study suggest the potential presence of both double-stranded and single-stranded DNA regulatory proteins within P. falciparum nuclear extract. As controversial as RNA interference remains in P. falciparum, this technique was used as a plausible knock-down strategy of parasite specific genes and certain trends, regarding the visible decreases in gene transcript level after double-stranded RNA treatment, were observed. However, final conclusions as to the feasibility of using RNA interference in P. falciparum remain to be elucidated. This study therefore ultimately lends insight into the transcriptional and post-transcriptional levels of P. falciparum gene regulation. / Dissertation (MSc)--University of Pretoria, 2010. / Biochemistry / unrestricted

A primary aim of Synthetic Biology is the design and implementation of biological systems that perform engineered functions. However, the assembly of double-stranded DNA molecules is a major barrier to this progress, as it remains time consuming and laborious. Here I present three improved methods for DNA assembly. The first is based on, and makes use of, BioBricks. The second method relies on overlap-extension PCR to assemble non-standard parts. The third method improves upon overlap extension PCR by reducing the number of steps and the time it takes to assemble DNA. Finally, I show how the PCR-based assembly methods presented here can be used, in concert, with in vivo homologous recombination in yeast to assemble as many as 19 individual DNA parts in one step. These methods will also be used to assemble an incoherent feedforward loop, gene regulatory network.

Les méthodes formelles informatiques constituent un outil très puissant pour la modélisation des réseaux génétiques et en particulier pour l'étude de leur dynamique. La modélisation discrète de René Thomas permet à la fois de représenter judicieusement les connaissances biologiques et d'utiliser les méthodes formelles. Cependant, ces modèles présentent deux limitations principales : la combinatoire sous-jacente ne permet pas de traiter des réseaux de très grande taille et les aspects chronométriques ne sont pas pris en compte. Cette thèse offre deux contributions respectivement liées à ces questions. La modélisation des réseaux génétiques commence par la sélection des entités les plus pertinentes pour la question abordée. Les réseaux obtenus restent souvent trop grands et nous cherchons donc à les réduire sans altérer les propriétés dynamiques importantes. Ici, nous définissons un cadre entièrement formel inspiré d'une technique d'Aurélien Naldi pour la suppression de variables et de seuils. Ces réductions conservent les comportements asymptotiques et permettent de prouver formellement l'équivalence asymptotique de différents modèles publiés d'un même réseau. Pour prendre en compte les informations chronométriques cruciales dans certains systèmes (e.g. cycle circadien), nous définissons un formalisme hybride fondé sur le formalisme de Thomas où les niveaux d'expression sont discrets, mais le temps continu. Ce cadre permet de construire un modèle abstrait de l'horloge circadienne des mammifères qui explique avec très peu de variables les propriétés de robustesse face à des changements de durées des alternances jour/nuit. / Formal methods from computer science constitute a powerful tool for the modelling of gene networks, including the study of their dynamics. The discrete modelling of René Thomas allows for a proper representation of biological knowledge as well as for use of formal methods. These models have two main limitations: the underlying combinatorics does not allow one to process very large networks, and the chronometric aspects are not taken into account. This thesis offers two contributions according to these issues. The design of gene network models begins with a selectiCalibrion of the most relevant entities. The resulting networks are often too large, and we show how to reduce them without altering the important dynamic properties. Here, we define a completely formal framework, inspired by a technique from Aurélien Naldi, driving the suppression of variables or thresholds. These reductions preserve the asymptotic behaviour. We formally prove the asymptotic equivalence of different published models for the same network. In order to take into account chronometric information that are crucial in some systems (e.g. circadian cycle), we define a hybrid formalism based on the Thomas' formalism where expression levels are discrete but time is continuous. This framework allows for the construction of an abstract model of the circadian clock in mammals. The model explains with very few variables the robustness of the system when submitted to duration changes of the day/night alternation.

Regulation of striated muscle differentiation and development are complex processes coordinated by an array of transcription factors. MEF2 is a crucial transcription factor required for muscle differentiation, but the roles of the individual MEF2 family members, MEF2A-D, have not been extensively evaluated. Acute ablation of Mef2 expression in skeletal myoblasts revealed a required role for MEF2A activity in myoblast differentiation that was not shared with the other MEF2 factors. We hypothesized that a transcriptomic level analysis of Mef2-deficient skeletal myoblasts would reveal distinct regulatory roles for each MEF2 isoform. Comparative microarray analysis supported our hypothesis and we observed distinct gene programs preferentially-sensitive to individual MEF2 isoforms. While there was no variance in the consensus binding site associated with regulation by individual MEF2 isoforms, we did observe uniquely enriched binding sites for candidate co-regulatory proteins that mediate these complex regulatory patterns. Based on our observations in skeletal myoblasts, we performed a series of acute Mef2 knockdowns in neonatal cardiomyocytes and uncovered a requirement for MEF2A and -D, but not MEF2C in cardiomyocyte survival. Comparative microarray analysis confirmed that, similar to skeletal myoblasts, the MEF2 family regulated distinct but overlapping gene programs in cardiomyocytes. Additionally, this analysis uncovered a previously uncharacterized antagonistic regulation of a subset of cell cycle and sarcomere genes. Interestingly, Mef2a and -d knockdowns caused an upregulation of cell cycle markers and downregulation of sarcomere genes, with the opposite regulatory pattern in Mef2c knockdown. Further investigation of the proximal promoter region of these genes revealed enriched binding sites for transcription factors associated with key signaling pathways in the developing embryo, Hedgehog and Notch. Overexpression of constitutively active components of these signaling pathways revealed that Notch function requires the presence of MEF2A and -D, while Hedgehog does not appear to interact with these two isoforms. We have shown through our studies that MEF2, a core muscle transcription factor, takes part in complex regulatory interactions that are critical for the appropriate development of striated muscle tissues. / 2018-08-09T00:00:00Z

Genes encode proteins, some of which in turn regulate other genes. Such interactions make up gene regulatory relationships or (dynamic) gene regulatory networks. With advances in the measurement technology for gene expression and in genome sequencing, it has become possible to measure the expression level of thousands of genes simultaneously in a cell at a series of time points over a specific biological process. Such time-course gene expression data may provide a snapshot of most (if not all) of the interesting genes and may lead to a better understanding gene regulatory relationships and networks. However, inferring either gene regulatory relationships or networks puts a high demand on powerful computational methods that are capable of sufficiently mining the large quantities of time-course gene expression data, while reducing the complexity of the data to make them comprehensible. This dissertation presents several computational methods for inferring gene regulatory relationships and gene regulatory networks from time-course gene expression. These methods are the result of the authors doctoral study. Cluster analysis plays an important role for inferring gene regulatory relationships, for example, uncovering new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. Two dynamic model-based clustering methods, namely the Markov chain model (MCM)-based clustering and the autoregressive model (ARM)-based clustering, are developed for time-course gene expression data. However, gene regulatory relationships based on cluster analysis are static and thus do not describe the dynamic evolution of gene expression over an observation period. The gene regulatory network is believed to be a time-varying system. Consequently, a state-space model for dynamic gene regulatory networks from time-course gene expression data is developed. To account for the complex time-delayed relationships in gene regulatory networks, the state space model is extended to be the one with time delays. Finally, a method based on genetic algorithms is developed to infer the time-delayed relationships in gene regulatory networks. Validations of all these developed methods are based on the experimental data available from well-cited public databases.

Genes encode proteins, some of which in turn regulate other genes. Such interactions make up gene regulatory relationships or (dynamic) gene regulatory networks. With advances in the measurement technology for gene expression and in genome sequencing, it has become possible to measure the expression level of thousands of genes simultaneously in a cell at a series of time points over a specific biological process. Such time-course gene expression data may provide a snapshot of most (if not all) of the interesting genes and may lead to a better understanding gene regulatory relationships and networks. However, inferring either gene regulatory relationships or networks puts a high demand on powerful computational methods that are capable of sufficiently mining the large quantities of time-course gene expression data, while reducing the complexity of the data to make them comprehensible. This dissertation presents several computational methods for inferring gene regulatory relationships and gene regulatory networks from time-course gene expression. These methods are the result of the authors doctoral study. Cluster analysis plays an important role for inferring gene regulatory relationships, for example, uncovering new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. Two dynamic model-based clustering methods, namely the Markov chain model (MCM)-based clustering and the autoregressive model (ARM)-based clustering, are developed for time-course gene expression data. However, gene regulatory relationships based on cluster analysis are static and thus do not describe the dynamic evolution of gene expression over an observation period. The gene regulatory network is believed to be a time-varying system. Consequently, a state-space model for dynamic gene regulatory networks from time-course gene expression data is developed. To account for the complex time-delayed relationships in gene regulatory networks, the state space model is extended to be the one with time delays. Finally, a method based on genetic algorithms is developed to infer the time-delayed relationships in gene regulatory networks. Validations of all these developed methods are based on the experimental data available from well-cited public databases.

Boolean networks are coarse-grained models of the regulatory dynamics that controls the survival and proliferation of a living cell. The dynamics is time- and state-discrete. This Boolean abstraction assumes that small differences in concentration levels are irrelevant; and the binary distinction of a low or a high concentration of each biomolecule is sufficient to capture the dynamics. In this work, we briefly introduce the gene regulatory models, where with the advent of system-specific Boolean models, new conceptual questions and analytical and numerical challenges arise. In particular, the response of the system to external intervention presents a novel area of research. Thus first we investigate how to quantify a node\\\'s individual impact on dynamics in a more detailed manner than an averaging against all eligible perturbations. Since each node now represents a specific biochemical entity, it is the subject of our interest. The prediction of nodes\\\' dynamical impacts from the model may be compared to the empirical data from biological experiments. Then we develop a hybrid model that incorporates both continuous and discrete random Boolean networks to compare the reaction of the dynamics against small as well as flip perturbations in different regimes. We show that the chaotic behaviour disappears in high sensitive Boolean ensembles with respect to continuous small fluctuations in contrast to the flipping. Finally, we discuss the role of distributing delays in stabilizing of the Boolean dynamics against noise. These studies are expected to trigger additional experiments and lead to improvement of models in gene regulatory dynamics.

Boolean networks are coarse-grained models of the regulatory dynamics that controls the survival and proliferation of a living cell. The dynamics is time- and state-discrete. This Boolean abstraction assumes that small differences in concentration levels are irrelevant; and the binary distinction of a low or a high concentration of each biomolecule is sufficient to capture the dynamics. In this work, we briefly introduce the gene regulatory models, where with the advent of system-specific Boolean models, new conceptual questions and analytical and numerical challenges arise. In particular, the response of the system to external intervention presents a novel area of research. Thus first we investigate how to quantify a node\\\''s individual impact on dynamics in a more detailed manner than an averaging against all eligible perturbations. Since each node now represents a specific biochemical entity, it is the subject of our interest. The prediction of nodes\\\'' dynamical impacts from the model may be compared to the empirical data from biological experiments. Then we develop a hybrid model that incorporates both continuous and discrete random Boolean networks to compare the reaction of the dynamics against small as well as flip perturbations in different regimes. We show that the chaotic behaviour disappears in high sensitive Boolean ensembles with respect to continuous small fluctuations in contrast to the flipping. Finally, we discuss the role of distributing delays in stabilizing of the Boolean dynamics against noise. These studies are expected to trigger additional experiments and lead to improvement of models in gene regulatory dynamics.

Development of the nervous system is a highly organized process that utilizes genetic mechanisms conserved across the animal kingdom. Components of the nervous system such as inhibitory GABAergic neural networks are common among most multicellular animals. The nematode Caenorhabditis elegans, utilizes similar genetic pathways to that of mice and humans to develop its GABAergic neural networks. These GABAergic neural networks are composed of two types of GABAergic motor neurons: the VD and DD sub-classes. The GABAergic differentiation of both these sub-classes requires the conserved transcription factor, Pitx/UNC-30. The VD sub-class is differentiated from the DD motor neurons by the expression of another transcription factor, COUP TFII/UNC-55. The transcriptional mechanisms regulating the expression of Pitx/UNC-30 and Coup TFII are unknown. We sought to determine how Pitx/UNC-30 and COUP TF-II/UNC-55 were transcriptionally regulated in an attempt to understand how mechanisms of GABAergic fate specification and class specification may be connected. We hypothesized there would be different mechanisms regulating the GABAergic differentiation and sub-class specification of the two sub-classes of GABAergic motor neurons. To test this, we dissected the transcriptional mechanisms responsible for the expression of Pitx/UNC-30 and COUP TFII/UNC-55. We found that different isoforms of the Hox cofactor Meis/UNC-62 stabilize and activate the expression of UNC-55. Furthermore, we conclude that Pitx/UNC-30 expression is regulated differently between the two motor neuron sub-classes by Meis/UNC-62, Hox-B7/MAB-5 and NeuroD/CND-1, each of which are vital to the development of different components of the nervous system in vertebrates. Our findings suggest that the GABAergic identity and the sub-class specification of neurons are under the control of multiple conserved transcription factors responsible for neuron fate determination and post mitotic identities.