Recent breakthroughs in high-throughput biotechnology have promoted the integration of multi-platform data to investigate signal transduction pathways within a cell. In order to model complicated dynamics and heterogeneity of biological pathways, sophisticated computational models are needed to address unique properties of both the biological hypothesis and the data. In this dissertation work, we have proposed and developed methods using Markov Chain Monte Carlo (MCMC) techniques to solve complex modeling problems in human cancer research by integrating multi-platform data. We focus on two research topics: 1) identification of transcriptional regulatory networks and 2) uncovering of aberrant intracellular signal transduction pathways. We propose a robust method, called GibbsOS, to identify condition specific gene regulatory patterns between transcription factors and their target genes. A Gibbs sampler is employed to sample target genes from the marginal function of outlier sum of regression t statistic. Numerical simulation has demonstrated significant performance improvement of GibbsOS over existing methods against noise and false positive connections in binding data. We have applied GibbsOS to breast cancer cell line datasets and identified condition specific regulatory rewiring in human breast cancer. We also propose a novel method, namely Gibbs sampler to Infer Signal Transduction (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. By converting predefined potential functions into a Gibbs distribution, GIST estimates edge directions by learning the distribution of linear signaling pathway structures. Through the sampling process, the algorithm is able to infer signal transduction directions which are jointly determined by both gene expression and network topology. We demonstrate the advantage of the proposed algorithms on simulation data with respect to different settings of noise level in gene expression and false-positive connections in protein-protein interaction (PPI) network. Another major contribution of the dissertation work is that we have improved traditional perspective towards understanding aberrant signal transductions by further investigating structural linkage of signaling pathways. We develop a method called Structural Organization to Uncover pathway Landscape (SOUL), which emphasizes on modularized pathways structures from reconstructed pathway landscape. GIST and SOUL provide a very unique angle to computationally model alternative pathways and pathway crosstalk. The proposed new methods can bring insight to drug discovery research by targeting nodal proteins that oversee multiple signaling pathways, rather than treating individual pathways separately. A complete pathway identification protocol, namely Infer Modularization of PAthway CrossTalk (IMPACT), is developed to bridge downstream regulatory networks with upstream signaling cascades. We have applied IMPACT to breast cancer treated patient datasets to investigate how estrogen receptor (ER) signaling pathways are related to drug resistance. The identified pathway proteins from patient datasets are well supported by breast cancer cell line models. We hypothesize from computational results that HSP90AA1 protein is an important nodal protein that oversees multiple signaling pathways to drive drug resistance. Cell viability analysis has supported our hypothesis by showing a significant decrease in viability of endocrine resistant cells compared with non-resistant cells when 17-AAG (a drug that inhibits HSP90AA1) is applied. We believe that this dissertation work not only offers novel computational tools towards understanding complicated biological problems, but more importantly, it provides a valuable paradigm where systems biology connects data with hypotheses using computational modeling. Initial success of using microarray datasets to study endocrine resistance in breast cancer has shed light on translating results from high throughput datasets to biological discoveries in complicated human disease studies. As the next generation biotechnology becomes more cost-effective, the power of the proposed methods to untangle complicated aberrant signaling rewiring and pathway crosstalk will be finally unleashed. / Ph. D.

With the abundance of biological data, computational prediction of gene regulatory networks (GRNs) from gene expression data has become more feasible. Although incorporating other prior knowledge (PK), along with gene expression, greatly improves prediction accuracy, the accuracy remains low. PK in GRN inference can be categorized into noisy and curated. Several algorithms were proposed to incorporate noisy PK, but none address curated PK. Another challenge is that much of the PK is not stored in databases or not in a unified structured format to be accessible by inference algorithms. Moreover, no GRN inference method exists that supports post-prediction PK. This thesis addresses those limitations with three solutions: PEAK algorithm for integrating both curated and noisy PK, Online-PEAK for post-prediction interactive feedback, and DeTangle for visualization and navigation of GRNs. PEAK integrates both curated as well as noisy PK in GRN inference. We introduce a novel method for GRN inference, CurInf, to effectively integrate curated PK, and we use the previous method, Modified Elastic Net, for noisy PK, and we call it NoisInf. Using 100% curated PK, CurInf improves the AUPR accuracy score over NoisInf by 27.3% in synthetic data, 86.5% in E. coli data, and 31.1% in S. cerevisiae data. Moreover, we developed an online algorithm, online-PEAK, that enables the biologist to interact with the inference algorithm, PEAK, through a visual interface to add their domain experience about the structure of the GRN as a feedback to the system. We experimentally verified the ability of online-PEAK to achieve incremental accuracy when PK is added by the user, including true and false PK. Even when the noise in PK is 10 times more than true PK, online-PEAK performs better than inference without any PK. Finally, we present DeTangle, a Web server for interactive GRN prediction and visualization. DeTangle provides a seamless analysis of GRN starting from uploading gene expression, GRN inference, post-prediction feedback using online-PEAK, and visualization and navigation of the predicted GRN. More accurate prediction of GRN can facilitate studying complex molecular interactions, understanding diseases, and aiding drug design. / Ph. D.

Transcription factors (TFs) have been a focal point of molecular biology research for decades, with evolving methodologies offering progressively deeper insights into their critical roles in gene regulation. Recent advancements in experimental and computational techniques have significantly enhanced our understanding of TF functionality, yet this depth of knowledge varies widely across the spectrum of known TFs — from extensively characterized ones with quantitative binding affinity data to those scarcely studied or understood. In this work, we systematically carried out binding and expression experiments on all Escherichia coli TFs using a standardized computational pipeline to identify direct and indirect regulatory targets. We further leveraged our binding data to develop a novel biophysically motivated neural network capable of predicting TF-DNA binding affinity from DNA sequence. This approach allowed us to design binding sites with specified affinities, including those stronger than any sequence observed in nature, which we validate experimentally using an in vitro binding assay. We further optimized this assay to provide insight into complex TF binding regimes, where chemical signals can modulate TF binding affinity. Finally, we demonstrate the utility of systematically mapping TF binding sites through a case study on a previously thought dormant TF acquired from viral infection, revealing an unexpected phenotype where it can hijack the host cell. This work not only offers broad insights into the determinants of TF binding and regulation, but also provides a means to predictively engineer binding sites with desired affinity, while demonstrating the power of efficient data processing in uncovering intricate biological processes. / 2025-05-23T00:00:00Z

À l'heure actuelle, il y a un regain d'intérêt pour Clostridium acetobutylicum, le biocatalyseur du procédé Weizmann historique, pour produire le n-butanol un produit chimique de commodité et un bio-carburant alternatif et renouvelable . Ce mémoire de thèse décrit un procédé de recombinaison homologue, utilisant plasmide réplicatif, pour la délétion ou l'introdu ction de gènes chez C. acetobutylicum avec une élimination facile des marqueurs utilisés. La souche de C. acetobutylicum cacl502upp et ce système de recombinaison homologue ont été utilisés dans d'autres expériences d'ingénierie pour obtenir une souche produisant du n-butanol avec une sélectivité élevée et en éliminant la plupart des co-produits. Le mutant final, C. acetobutylicum (C. acetobutylicum CAB1060) a été généré avec succès. Cette souche CAB1060 a été utilisée dans un nouveau procédé de fermentation continu qui utilise i) l'extraction in situ des alcools par distillation sous pression réduite et ii) des cultures à haute densité cellulaire (et ne faisant pas intervenir de procédé membranaire) pour atteindre des titre, rendement et productivité en n-butanol qui n'ont jam ais été obtenus chez aucun micro-organisme.Un second procédé de recombinaison homologue utilisant un plasmide non réplicatif pour la modification de gène sans marqueur est également décrit dans le présent mémoire. Cette méthode permet d'inactiver simultaném ent deux gènes. Il a été utilisé avec succès pour la construction d'un mutant incapable de produire de l'hydrogène et utile, comme souche plate-forme, pour l'ingénierie de C. acetobutylicum pour produire en continu des produits chimiques de commodité et des bio­ carburants. / Current ly, there is a resurgence of interest in Clostridium acetobutylicum, the biocatalyst of the historical Weizmann process, to produce n-butanol for use both as a bulk chemical and as a renewablc alternative transportation fuel. This thesis describes a method of homologous recombination by replicative plasmid to delete or introduce genes in C. acetobutylicum . This method was successfull y used to delete genes, includin g CACJ502, CAC3535, CAC2879 (upp), to generate C. acetobutylicum. These strains are readily transformable without any previous plasmid methylation and can serve as hosts for a "marker-less" genetic exchange system. A mutant C. acetobutylicum (C. acetobuty licum CAB 1060) was successfully genera ted. This final mutant produces mainly bu tanol, with ethanol and traces of acetate at a molar rati o of 7:1 :1 . This CAB 1060 strain was subjected to a new continuous fermentation process using i) in situ extraction of alcohols by distillation under low pressure and ii) high cell density cultures to increase the titer, yield and productivity of n-butanol production to levels that have never been previously açhieved in any organism . A second homologous recombination method using non-replicative plasmid for marker less gene modification is also described in this thesis. This method allows the simultaneou s inactivation of two genes. lt has been successfully used to construct a mutant unable to produce hydrogen and useful, as a platform strain, for further engineering of C. acetobutylicum to continuously produce bulk chemicals and fuels.

Le but de cette thèse est de préciser les mécanismes moléculaires agissant en aval du facteur de transcription CROWN ROOT LESS 1 (CRL1) qui régule la formation des racines coronaires (RC). Nous avons pu identifier dans un premier temps une nouvelle séquence d’ADN reconnue par CRL1 nommé CRL1-box différente de la LBD-box qui était le seul motif cis-régulateur précédemment décrit pour la famille des facteurs de transcription (FT) de type LATERAL ORGAN BOUNDARIES DOMAIN (LBD). Nous avons ensuite identifié un groupe de gènes régulés par CRL1, et avons montré l’implication de deux d’entre eux, OsROP et OsbHLH044, dans le développement des RC. OsbHLH044 est un facteur de transcription répresseur et semble être aussi impliqué dans la sénescence cellulaire ainsi que la réponse aux stress. Enfin, nous avons mis en évidence une cascade de régulation liant positivement CRL1 avec QUIESCENT-CENTER-SPECIFIC HOMEOBOX (QHB) un gène impliqué dans la différenciation et le maintien du centre quiescent via le facteur de transcription OsHOX14. En addition nous avons mis en évidence une boucle de rétroaction négative de QHB sur ses activateurs CRL1 et OsHOX14, qui pourrait être impliquée dans la structuration du primordia de racine coronaire. / The aim of this thesis is to specify the molecular mechanisms acting downstream of the CROWN ROOT LESS 1 transcription factor (CRL1) that regulates coronary root (CR) formation. We were able to identify at first a new CRL1 recognized DNA sequence named CRL1-box different from the LBD-box which was the only cis-regulatory motif previously described for the LATERAL ORGAN BOUNDARIES DOMAIN (LBD) transcription factor (TF) family. We then identified a group of genes regulated by CRL1, and showed the involvement of two of them, OsROP and OsbHLH044, in the development of CR. OsbHLH044 is a repressive transcription factor and appears to be also involved in cell senescence as well as stress response. Finally, we demonstrated a regulatory cascade linking CRL1 with QUIESCENT-CENTER-SPECIFICHOMEOBOX (QHB), a gene involved in the differentiation and maintenance of the quiescent center, via the OsHOX14 transcription factor. In addition we have demonstrated a negative feedback loop of QHB on its activators CRL1 and OsHOX14, which could be involved in structuring the coronary root primordia

<p>Specification and differentiation of a cell is accomplished by changing its gene expression profiles. These processes require temporally and spatially regulated transcription factors (TFs), to induce the genes that are necessary to a specific cell type. In each cell a set of TFs interact with each other or activate their targets; as development progresses, transcription factors receive regulatory inputs from other TFs and a complex gene regulatory network (GRN) is generated. Adding complexity, each TF can be regulated not only at the transcriptional level, but also by translational, and post-translational mechanisms. Thus, understanding a developmental process requires understanding the interactions between TFs, signaling molecules and target genes which establish the GRN.</p><p>In this thesis, two genes, FoxN2/3, a TF and FGFR1, a component of the FGF signaling pathway are investigated. FoxN2/3 and FGFR1 have different mechanisms that function in sea urchin development; FoxN2/3 regulates gene expression and FGFR1 changes phosphorylation of target proteins. However, their ultimate goals are the same: changing the state of an earlier GRN into the next GRN state. </p><p>First, we characterize FoxN2/3 in the primary mesenchyme cell (PMC) GRN. Expression of foxN2/3 begins in the descendants of micromeres at the early blastula stage; and then is lost from PMCs at the mesenchyme blastula stage. foxN2/3 expression then shifts to the secondary mesenchyme cells (SMCs) and later to the endoderm. Here we show that, Pmar1, Ets1 and Tbr are necessary for activation of foxN2/3 in the descendants of micromeres. The later endomesoderm expression is independent of the earlier expression of FoxN2/3 in micromeres and independent of signals from PMCs. FoxN2/3 is necessary for several steps in the formation of larval skeleton. A number of proteins are necessary for skeletogenesis, and early expression of at least several of these is dependent on FoxN2/3. Furthermore, knockdown (KD) of FoxN2/3 inhibits normal PMC ingression. PMCs lacking FoxN2/3 protein are unable to join the skeletogenic syncytium and they fail to repress the transfating of SMCs into the skeletogenic lineage. Thus, FoxN2/3 must be present for the PMC GRN to control normal ingression, expression of skeletal matrix genes, prevention of transfating, and control fusion of the PMC syncytium.</p><p>Second, we show that the FGF-FGFR1 signaling is required for the oral-aboral axis formation in the sea urchin embryos. Without FGFR1, nodal is induced in all of the cells at the early blastula stage and this ectopic expression of nodal requires active p38 MAP kinase. The loss of oral restriction of nodal expression results in the abnormal organization of PMCs and the larval skeleton; it also induces ectopic expression of oral-specific genes and represses aboral-specific genes. The abnormal oral-aboral axis formation also affected fgf and vegf expression patterns; normally these factors are expressed in two restricted areas of the ectoderm between the oral and the aboral side, but when FGFR1 is knocked down, Nodal expands, and in response the expression of the FGF and VEGF ligands expands, and this in turn affects the abnormal organization of larval skeleton.</p> / Dissertation

Fluctuations, or "noise", can play a key role in determining the behaviour of living systems. The molecular-level fluctuations that occur in genetic networks are of particular importance. Here, noisy gene expression can result in genetically identical cells displaying significant variation in phenotype, even in identical environments. This variation can act as a basis for natural selection and provide a fitness benefit to cell populations under stress. This thesis focuses on the development of new conceptual knowledge about how gene expression noise and gene network topology influence drug resistance, as well as new simulation techniques to better understand cell population dynamics. Network topology may at first seem disconnected from expression noise, but genes in a network regulate each other through their expression products. The topology of a genetic network can thus amplify or attenuate noisy inputs from the environment and influence the expression characteristics of genes serving as outputs to the network. The main body of the thesis consists of five chapters: 1. A published review article on the physical basis of cellular individuality. 2. A published article presenting a novel method for simulating the dynamics of cell populations. 3. A chapter on modeling and simulating replicative aging and competition using an object-oriented framework. 4. A published research article establishing that noise in gene expression can facilitate adaptation and drug resistance independent of mutation. 5. An article submitted for publication demonstrating that gene network topology can affect the development of drug resistance. These chapters are preceded by a comprehensive introduction that covers essential concepts and theories relevant to the work presented.

Les cellules immunitaires proviennent d'un ensemble commun de cellules souches hématopoïétiques qui se différencient hiérarchiquement en lignées myéloïdes et lymphoïdes. Ce processus est étroitement régulé par un réseau entrelacé de facteurs de transcription et de régulateurs épigénétiques, qui contrôlent l'activation et la répression des gènes impliqués. Les travaux récents sur la reprogrammation cellulaire ont montré que certaines protéines peuvent reprogrammer des cellules différenciées, comme le facteur de transcription C/EBPa qui peut induire la trans-differenciation de cellules B en macrophages. De plus, une courte induction de Cebpa suivie de l’expression des quatre facteurs de transcription Oct4-Sox2-Klf4-cMyc permet une reprogrammation extrêmement rapide en cellules pluripotentes. Afin de déchiffrer le réseau de régulation moléculaire contrôlant la spécification et la reprogrammation des cellules immunitaires, j’ai combiné différentes méthodes à haut débit pour analyser l’expression des gènes et leur régulation épigénétique, et ce au court de la reprogrammation des cellules B. J’ai découvert des interactions entre différents facteurs de transcription, au niveau des régions régulatrices de gènes des différents programmes génétiques impliqués (lymphoide, myeloide et pluripotence), et j’ai identifié des facteurs régulant l’état de la chromatine également impliqués dans la reprogrammation (notamment Lsd1, Hdac1, Brd4 et Tet2). Enfin, J’ai intégré ces données dans un modèle dynamique du réseau moléculaire régulant la spécification des cellules B et des macrophages à partir de progéniteurs multipotents. J’ai utilisé à la fois des méthodes analytiques (analyse des états stables) et des simulations (simulations logiques asynchrones, chaînes de Markov à temps continu) pour étudier in silico la différenciation et la reprogrammation cellulaire. Ces analyses ont révélés des régulations transcriptionelles encore inconnues, que nous avons pu confirmer expérimentalement. Nous avons ainsi obtenu une meilleure compréhension des circuits de régulation contrôlant le destin cellulaire. / Immune cells arise from a common set of hematopoietic stem cells, which differentiate hierarchically into the myeloid and lymphoid lineages. This process is tightly regulated by an intertwined network of transcription and epigenetic factors, which control both the activation and repression of gene programs, to ensure cell commitment. However, recent work on cellular reprogramminghas shown that the ectopic expression of some specific factors can enforce the trans-differentiation of committed cells. The transcription factor C/EBPa can induce the reprogramming of B-cells into macrophages. Furthermore, a pulse of Cebpa expression in B cells followed by the expression of the four transcription factors Oct4-Sox2-Klf4-cMyc leads to an extremely fast and efficient reprogramming into induced pluripotent stem cells. Despite the many data we have on the molecular mechanisms by which specific genes are regulated, we are still lacking a global understanding of the interplay between these factors and how theycontrol cell fate. In order to decipher the molecular regulatory network controlling immune cell specification and their reprogramming, I have combined a variety of high-throughput methods to measure changes in gene expression and epigenetic regulation during B cells reprogramming. I have revealed the interplay between different transcription factors at enhancers regulating genes of the different programs (B cells, macrophages and pluripotent cells) and identified epigenetic regulators forming complexes and controlling enhancers activities (such as Lsd1, Hdac1, Brd4 and Tet2) and consequently regulating cell fate. Finally, I integrated these data together with published data, in a computational model of the regulatory network controlling the specification of B-cells and macrophages from multipotent progenitors. I used both analytic tools (stable states analysis) and simulations (logical asynchronous simulations, continuous time Markov chains) to study in silico differentiation and reprogramming.These analyses have revealed previouslyunknown transcriptional regulations, which weconfirmed experimentally, and allowed us to get abetter understanding of the regulatory circuitscontrolling cell fate commitment.

Bipolar disorder (BD) is a severe psychiatric illness with a consistent genetic influence, involving complex interactions between numerous genes and environmental factors. Immediate early genes (IEGs) are activated in the brain in response to environmental stimuli, such as stress. The potential to translate environmental stimuli into long-term changes in brain has led to increased interest in a potential role for these genes influencing risk for psychiatric disorders. Our recent finding using network-based approach has shown that the regulatory unit of early growth response gene 3 (EGR3) of IEGs family was robustly repressed in postmortem prefrontal cortex of BD patients. As a central transcription factor, EGR3 regulates an array of target genes that mediate critical neurobiological processes such as synaptic plasticity, memory and cognition. Considering that EGR3 expression is induced by brain-derived neurotrophic factor (BDNF) that has been consistently related to BD pathophysiology, we suggest a link between BDNF and EGR3 and their potential role in BD. A growing body of data from our group and others has shown that peripheral BDNF levels are reduced during mood episodes and also with illness progression. In this same vein, BDNF has been proposed as an important growth factor in the impaired cellular resilience related to BD. Taken together with the fact that EGR3 regulates the expression of the neurotrophin receptor p75NTR and may also indirectly induce BDNF expression, here we propose a feed-forward gene regulatory network involving EGR3 and BDNF and its potential role in BD.

Fluctuations, or "noise", can play a key role in determining the behaviour of living systems. The molecular-level fluctuations that occur in genetic networks are of particular importance. Here, noisy gene expression can result in genetically identical cells displaying significant variation in phenotype, even in identical environments. This variation can act as a basis for natural selection and provide a fitness benefit to cell populations under stress. This thesis focuses on the development of new conceptual knowledge about how gene expression noise and gene network topology influence drug resistance, as well as new simulation techniques to better understand cell population dynamics. Network topology may at first seem disconnected from expression noise, but genes in a network regulate each other through their expression products. The topology of a genetic network can thus amplify or attenuate noisy inputs from the environment and influence the expression characteristics of genes serving as outputs to the network. The main body of the thesis consists of five chapters: 1. A published review article on the physical basis of cellular individuality. 2. A published article presenting a novel method for simulating the dynamics of cell populations. 3. A chapter on modeling and simulating replicative aging and competition using an object-oriented framework. 4. A published research article establishing that noise in gene expression can facilitate adaptation and drug resistance independent of mutation. 5. An article submitted for publication demonstrating that gene network topology can affect the development of drug resistance. These chapters are preceded by a comprehensive introduction that covers essential concepts and theories relevant to the work presented.