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.

Em sistemas biológicos, o estudo da estabilidade das redes de regulação gênica é visto como uma contribuição importante que a Matemática pode proporcionar a pesquisas sobre câncer e outras doenças genéticas. Neste trabalho, utilizamos o conceito de ``canalização\'\' como sinônimo de estabilidade em uma rede biológica. Como as características de uma rede de regulação canalizada ainda são superficialmente compreendidas, estudamos esse conceito sob o ponto de vista computacional: propomos um modelo matemático simplificado para descrever o fenômeno e realizamos algumas análises sobre o mesmo. Mais especificamente, a estabilidade da maior bacia de atração das redes Booleanas - um clássico paradigma para a modelagem de redes de regulação - é analisada. Os resultados indicam que a estabilidade da maior bacia de atração está relacionada com dados biológicos sobre o crescimento de colônias de leveduras e que considerações sobre a interação entre as funções Booleanas e a topologia da rede devem ser realizadas conjuntamente na análise de redes estáveis. / In biological systems, the study of gene regulatory networks stability is seen as an important contribution that Mathematics can make to cancer research and that of other genetic diseases. In this work, we consider the concept of ``canalization\'\' as a consequence of stability in gene regulatory networks. The characteristics of canalized regulatory networks are superficially understood. Hence, we study the canalization concept under a computational framework: a simplified model is proposed to describe the phenomenon using Boolean Networks - a classical paradigm to modeling regulatory networks. Specifically, the stability of the largest basin of attraction in gene regulatory networks is analyzed. Our results indicate that the stability of the largest basin of attraction is related to biological data on growth of yeast colonies, and that thoughts about the interaction between Boolean functions and network topologies must be given in the analysis of stable networks.

<div>In the past several decades, the advent of high-throughput biotechnologies for genomics study provides appealing opportunities for us to understand the complex gene interaction inside biological systems, attracting many researches in constructing gene regulatory networks (GRNs). Motivated by the promise of the genetical genomics</div><div>study, our research group has recently focused on representing gene regulatory networks using structural equation models and further revealing system-wide gene regulations.This dissertation presents two recent works along this direction.</div><div><br></div><div><div>Firstly, we conducted thorough theoretical analysis of the recently proposed Two-Stage Penalized Least Squares (2SPLS) method for constructing large systems of structural equation models. We establish the estimation and prediction error bounds for results at both stages of 2SPLS as well as its variable selection consistency. Specically, a bounded eigenvalue assumption is imposed to ensure the consistency properties of the ^l2-penalized regressions at the first stage. At the second stage, the estimation and</div><div>variable selection consistency of the ^l1-penalized regressions are obtained by assuming a restricted eigenvalue condition and a variant of irrepresentable condition, which are both commonly employed in the current literature. We will show that the 2SPLS estimator works not only for fixed dimensions but also diverging dimensions which can grow to infinity with the sample size but at an appropriate rate.</div></div><div><br></div><div><div>Secondly, we developed a novel statistical method to identify structural differences between two cognate networks characterized by structural equation models. We</div><div>propose to reparameterize the model to separate the differential structures from common structures, and then design an algorithm with calibration and construction stages to identify these differential structures directly. The calibration stage serves to obtain consistent prediction by building the^l2 regularized regression of each endogenous</div><div>variables against pre-screened exogenous variables, correcting for potential endogeneity issue. The construction stage consistently selects and estimates both common and</div><div>differential effects by undertaking ^l1 regularized regression of each endogenous variable against the predicts of other endogenous variables as well as its anchoring exogenous</div><div>variables. Our method allows for easy parallel computation. Theoretical results are obtained to establish non-asymptotic error bounds of predictions and estimates at both stages. Our studies on simulated data demonstrated that the proposed method performed much better than independently constructing networks. A real data set</div><div>was analyzed to illustrate the applicability of our method.</div></div>

This dissertation develops and analyzes differential equation-based mathematical models and efficient numerical methods and algorithms for genetic regulatory network identification. The primary objectives of the dissertation are to design, analyze, and test a general variational framework and numerical methods for seeking its approximate solutions for reverse engineering genetic regulatory networks from microarray datasets using the approach based on differential equation modeling. In the proposed variational framework, no structure assumption on the genetic network is presumed, instead, the network is solely determined by the microarray profile of the network components and is identified through a well chosen variational principle which minimizes a biological energy functional. The variational principle serves not only as a selection criterion to pick up the right biological solution of the underlying differential equation model but also provide an effective mathematical characterization of the small-world property of genetic regulatory networks which has been observed in lab experiments. Five specific models within the variational framework and efficient numerical methods and algorithms for computing their solutions are proposed and analyzed in the dissertation. Model validations using both synthetic network datasets and real world subnetwork datasets of Saccharomyces cerevisiae (yeast) and E. Coli are done on all five proposed variational models and a performance comparison vs some existing genetic regulatory network identification methods is also provided. As microarray data is typically noisy, in order to take into account the noise effect in the mathematical models, we propose a new approach based on stochastic differential equation modeling and generalize the deterministic variational framework to a stochastic variational framework which relies on stochastic optimization. Numerical algorithms are also proposed for computing solutions of the stochastic variational models. To address the important issue of post-processing computed networks to reflect the small-world property of underlying genetic regulatory networks, a novel threshholding technique based on the Random Matrix Theory is proposed and tested on various synthetic network datasets.

Epithelial-mesenchymal transitions (EMTs) are fundamental and indispensable to embryonic morphogenesis throughout the animal kingdom. At the onset of gastrulation in the sea urchin embryo, micromere-derived primary mesenchyme cells (PMCs) undergo an EMT process to ingress into the blastocoel, and these cells later become the larval skeleton. Much has been learned about PMC specification in sea urchin embryos. However, much less is known about how states of the sequentially progressing PMC gene regulatory network (GRN) controls the EMT process during PMC ingression. Transcriptional regulators such as Snail and Twist have emerged as important molecules for controlling EMTs in many model systems. Sea urchin snail and twist genes were cloned from Lytechinus variegates, and each has been experimentally connected to the PMC regulatory network; these experiments demonstrate several requirements for PMC ingression, and in doing so, begin to illustrate how a gene regulatory network state controls morphogenesis. Functional knockdown analyses of Snail with morpholino-substituted antisense oligonucleotides (MASO) in whole embryos and chimeras demonstrated that Snail is required in micromeres for PMC ingression. Investigations also show that Snail downregulates cadherin expression as an evolutionarily conserved mechanism, and Snail positively regulates a required endocytic clearance of epithelial membrane molecules during EMT. Perturbation experiments indicate that Twist has accessory roles in regulating PMC ingression, and possibly plays a maintenance role in PMC specification network state. In addition, Twist also functions in the post-EMT network state, particularly in directing PMC differentiation and skeletogenesis. The recently annotated sea urchin genome accelerates the discovery of new genes and holds strong promise of mapping out a complete canvas of the micromere-PMC gene regulatory network. Using the genome resources we successfully cloned several newly identified PMC genes, and found most of them to be expressed in micromeres just prior to ingression of the nascent PMCs. Current experiments focus on the roles of these genes in preparing for, executing, and/or controlling the mesenchymal behavior following PMC ingression. The functions and inter-relationships of these genes will greatly augment our understanding of how a gene regulatory network state controls a crucial morphogenetic event. / Dissertation

No description available.

Many vertebrate organs form via the sequential, reciprocal exchange of signaling molecules between juxtaposed epithelial (E) and mesenchymal (M) tissues. For example, the instructive signaling potential for tooth development (odontogenesis) resides in the dental epithelium at the initiation-stage, and subsequently shifts to the dental mesenchyme one day later at the bud-stage. However, the properties of the gene regulatory networks (GRNs) that control the signaling dynamics during epithelial-mesenchymal (E-M) interactions in organogenesis are largely unknown. This dissertation describes an interdisciplinary effort between developmental and systems biology to elucidate the E-M GRN that controls early odontogenesis. The results provide a molecular mechanism for the longstanding paradigm of sequential, reciprocal E-M tissue interactions in development. We generated large-scale spatiotemporal gene expression data for the developing mouse tooth. Surprisingly, the shift in signaling molecule expression from E to M is accompanied by a striking concordance in genome-wide expression changes in both E-M compartments as development proceeds. We hypothesized that since diffusible signaling molecules can act on either E or M independent of their tissue site of synthesis, signaling molecules are uniquely able to simultaneously synchronize and couple the transcriptional dynamics and hence the developmental progression of E and M. To identify the unifying mechanism behind concordant E and M genome-wide expression changes in the face of the discordant expression changes in signaling molecule expression, we developed a novel probabilistic technique that integrates regulatory evidence from microarray gene expression data and the literature to determine the E-M GRN for early tooth development. This GRN contains a uniquely configured E-M Wnt/Bmp feedback circuit in which the Wnt and Bmp signaling pathways in E cross-regulate the expression of Wnt and Bmp4 signaling molecules, whereas both pathways jointly regulate Bmp4 expression in M. We validated the Wnt/Bmp feedback circuit in vivo using compound genetic mutations in mice that either short-circuit or break the circuit, and used mathematical modeling to show how the structure of the Wnt/Bmp feedback circuit can account for reciprocal signaling dynamics. Collectively, these results provide a simple mechanistic framework for how simultaneous signal transduction in E-M compartments can account for the signaling dynamics in organogenesis.

The basis of developmental biology lies in the idea of when and how cells decide to divide or to differentiate. Previous studies have established several signaling pathways that determine cell fate decisions, including Notch, Wingless, Hedgehog, Bone morphogenetic protein, and Fibroblast growth factor. Signaling converges on transcriptional factors that regulate gene expression. In mouse embryonic stem cells, I explored how pluripotency and differentiation are regulated through opposing actions of beta-catenin-mediated canonical Wnt signaling, and the mechanisms underlying Sonic hedgehog signaling in generating progenitor cells in the ventral neural tube.

Σκοπός της συγκεκριμένης ερευνητικής εργασίας ήταν η αποκάλυψη του ρόλου και της θέσης του PlCOUP-TF στο ρυθμιστικό γονιδιακό δίκτυο της νευρογένεσης του αχινού Paracentrotus lividus, αλλά και η διερεύνηση των επιδράσεων του συγκεκριμένου μεταγραφικού παράγοντα στα υπόλοιπα γονίδια που εμπλέκονται στην δημιουργία νευρώνων. Ο PlCOUP-TF είναι ένας μητρικός μεταγραφικός παράγοντας και ορφανός πυρηνικός υποδοχέας που φαίνεται να διαδραματίζει σημαντικό ρόλο στην δημιουργία νευρώνων σε μια ποικιλία οργανισμών. Τα υπό μελέτη γονίδια της νευρογένεσης είναι επίσης μεταγραφικοι παράγοντες με εξελικτικά συντηρημένες επικράτειες. Για τον προαναφερθέντα σκοπό, πραγματοποίηθηκαν πειράματα διπλής φθορίζουσας in situ υβριδοποίησης ώστε να αποκαλυφθούν οι περιοχές συνεντοπίσμου του PlCOUP-TF και των γονιδίων της νευρογένεσης σε ώριμα στάδια της ανάπτυξης. Επίσης, διεξήχθησαν πειράματα καταστολής της μητρικής έκφρασης του PlCOUP-TF με ενέσεις με ΜΑSO σε γονιμοποιημένα ωάρια και έλεγχος της έκφρασης των νευροειδικών γονιδίων στα ενεμένα και στα αντίστοιχα control έμβρυα. Ο έλεγχος της έκφρασης ήταν τόσο ποιοτικός (in situ χρωμογόνος υβριδοποίηση) όσο και ποσοτικός (Q-PCR). Με τα πειράματα αυτά, αποκαλύφθηκε ότι πιθανότατα ο συγκεκριμένος μεταγραφικός παράγοντας λειτουργεί ενεργοποιητικά για τα εν λόγω γονίδια. Συγκεκριμένα, φαίνεται η ύπαρξη κατασταλτικής δράσης του PlCOUP-TF πάνω σε καταστολέα των γονιδίων της νευρογένεσης (double negative gate) στην περιοχή του εμπρόσθιου νευροεξωδέρματος και σε συγκεκριμένες περιοχές της βλεφαριδωτής ζώνης. / Our aim was to identify the role of the orphan nuclear receptor PlCoup-TF in sea urchin embryonic neurogenesis and especially in the determination of the anterior neuroectoderm (ANE). To this end, we cloned a set of embryonic cDNAs encoding regulatory proteins expressed specifically in the ANE and we prepared antisense RNA probes for double fluorescence in situ hybridization for the following genes: PlCoup-TF, PlHbn, Plz81 and PlFoxG. We studied the spatial pattern of expression of ANE genes in conjunction with the expression pattern of PlCoup-TF in all embryonic stages of the sea urchin Paracentrotus lividus. A neurogenic territory specified within the animal pole of the embryo, is formed as a result of the interplay of the aforementioned regulatory factors that together constitute a sub-circuit within the embryonic gene regulatory network (GRN). We wanted to determine PlCoup-TF’s place within the GRN and specifically the ANE sub-circuit. Therefore, we knockdowned PlCoup-TF expression during embryogenesis by injecting specific morpholino antisense oligonucleotides (MASO) into sea urchin eggs and determine the expression pattern of the ANE specific genes by chromogenic in situ hybridization to resulting morphant embryos. The efficiency of the knockdown was measured by QPCR, where the amount of PlCoup-TF transcripts of morphants is compared to that of control embryos. Finally, we concluded that probably PlCoup-TF activate the ANE genes, by repressing repressor(s) of these genes (double negative gate) in ANE and in specific regions of CBE.