Capturing the mechanism of gene regulation in a living cell is essential to predict the behavior of cell in response to intercellular or extra cellular factors. Such prediction capability can potentially lead to development of improved diagnostic tests and therapeutics [21]. Amongst reverse engineering approaches that aim to model gene regulation are Dynamic Bayesian Networks (DBNs). DBNs are of particular interest as these models are capable of discovering the causal relationships between genes while dealing with noisy gene expression data. At the same time, the problem of discovering the optimum DBN model, makes structure learning of DBN a challenging topic. This is mainly due to the high dimensionality of the search space of gene expression data that makes exhaustive search strategies for identifying the best DBN structure, not practical. In this work, for the first time the application of a covariance-based evolutionary search algorithm is proposed for structure learning of DBNs. In addition, the convergence time of the proposed algorithm is improved compared to the previously reported covariance-based evolutionary search approaches. This is achieved by keeping a fixed number of good sample solutions from previous iterations. Finally, the proposed approach, M-CMA-ES, unlike gradient-based methods has a high probability to converge to a global optimum. To assess how efficient this approach works, a temporal synthetic dataset is developed. The proposed approach is then applied to this dataset as well as Brainsim dataset, a well known simulated temporal gene expression data [58]. The results indicate that the proposed method is quite efficient in reconstructing the networks in both the synthetic and Brainsim datasets. Furthermore, it outperforms other algorithms in terms of both the predicted structure accuracy and the mean square error of the reconstructed time series of gene expression data. For validation purposes, the proposed approach is also applied to a biological dataset composed of 14 cell-cycle regulated genes in yeast Saccharomyces Cerevisiae. Considering the KEGG1 pathway as the target network, the efficiency of the proposed reverse engineering approach significantly improves on the results of two previous studies of yeast cell cycle data in terms of capturing the correct interactions. / Thesis (Master, Computing) -- Queen's University, 2008-09-09 11:35:33.312

A basic issue for translational genomics is to model gene interactions via gene regulatory networks (GRNs) and thereby provide an informatics environment to derive and study effective interventions eradicating the tumor. In this dissertation, we present two different approaches to intervention methods in cancer-related GRNs. Decisions regarding possible interventions are assumed to be made at every state transition of the network. To account for dosing constraints, a model for the sequence of treatment windows is considered, where treatments are allowed only at the beginning of each treatment cycle followed by a recovery phase. Due to biological variabilities within tumor cells, the action period of an antitumor drug can vary among a population of patients. That is, a treatment typically has a random duration of action. We propose a unified approach to such intervention models for any Markovian GRN governing the tumor. To accomplish this, we place the problem in the general framework of partially controlled decision intervals with infinite horizon discounting cost. We present a methodology to devise optimal intervention policies for synthetically generated gene regulatory networks as well as a mutated mammalian cell-cycle network. As a different approach, we view the phenotype as a characterization of the long- run behavior of the Markovian GRN and desire interventions that optimally move the probability mass from undesirable to desirable states. We employ a linear programming approach to formulate the maximal shift problem, that is, optimization is directly based on the amount of shift. Moreover, the same basic linear programming structure is used for a constrained optimization, where there is a limit on the amount of mass that may be shifted to states that are not directly undesirable relative to the pathology of interest, but which bear some perceived risk. We demonstrate the performance of optimal policies on synthetic networks as well as two real GRNs derived from the metastatic melanoma and mammalian cell cycle. These methods, as any effective cancer treatment must, aim to carry out their actions rapidly and with high efficiency such that a very large percentage of tumor cells die or shift into a state where they stop proliferating.

Inference of regulatory networks has received attention of researchers from many fields. The challenge offered by this problem is its being a typical modeling problem under insufficient information about the process. Hence, we need to derive the apriori unavailable information from the empirical observations. Modeling by inference consists of selecting or defining the most appropriate model structure and inferring the parameters. An appropriate model structure should have the following properties. The model parameters should be inferable. Given the observation and the model class, all parameters used in the model should have a unique solution restriction of the solution space). The forward model should be accurately computable (restriction of the solution space). The model should be capable of exhibiting the essential qualitative features of the system (limit of the restriction). The model should be relevant with the process (limit of the restriction). A piecewise linear formulation, described by a switching state transition matrix and a switching state transition vector with a Boolean function indicating the switching conditions is proposed for the inference of gene regulatory networks. This thesis mainly concerns using a formulation of switching networks obeying all the above mentioned requirements and developing an inference algorithm for estimating the parameters of the formulation. The methodologies used or developed during this study are applicable to various fields of science and engineering.

In a microarray experiment, it is expected that there will be correlations between the expression levels of different genes under study. These correlation structures are of great interest from both biological and statistical points of view. From a biological perspective, the identification of correlation structures can lead to an understanding of genetic pathways involving several genes, while the statistical interest, and the emphasis of this thesis, lies in the development of statistical methods to identify such structures. However, the data arising from microarray studies is typically very high-dimensional, with an order of magnitude more genes being considered than there are samples of each gene. This leads to difficulties in the estimation of the dependence structure of all genes under study. Graphical models and Bayesian networks are often used in these situations, providing flexible frameworks in which dependence structures for high-dimensional data sets can be considered. The current methods for the estimation of dependence structures for high-dimensional data sets typically assume the presence of independent and identically distributed samples of gene expression values. However, often the data available will have a complex mean structure and additional components of variance. Given such data, the application of methods that assume independent and identically distributed samples may result in incorrect biological conclusions being drawn. In this thesis, methods for the estimation of Bayesian networks for gene expression data sets that contain additional complexities are developed and implemented. The focus is on the development of score metrics that take account of these complexities for use in conjunction with score-based methods for the estimation of Bayesian networks, in particular the High-dimensional Bayesian Covariance Selection algorithm. The necessary theory relating to Gaussian graphical models and Bayesian networks is reviewed, as are the methods currently available for the estimation of dependence structures for high-dimensional data sets consisting of independent and identically distributed samples. Score metrics for the estimation of Bayesian networks when data sets are not independent and identically distributed are then developed and explored, and the utility and necessity of these metrics is demonstrated. Finally, the developed metrics are applied to a data set consisting of samples of grape genes taken from several different vineyards. / Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 2010

Η φυσιολογική ανάπτυξη του εμβρύου των θηλαστικών επιτυγχάνεται μέσα από τη συντονισμένη ρύθμιση της γονιδιακής έκφρασης των κυττάρων που προκύπτουν από τις διαιρέσεις του ζυγωτού. Έτσι λαμβάνει χώρα ο σταδιακός καθορισμός της τύχης των πολυδύναμων αυτών κυττάρων, τα οποία προσανατολίζονται κατάλληλα κατά μήκος του πρόσθιο-οπίσθιου και του ραχιαίο-κοιλιακού άξονα του εμβρύου. Ο κατάλληλος προσανατολισμός εξασφαλίζει την έκθεση των κυττάρων σε τοποειδικά εκφραζόμενους μεταγραφικούς παράγοντες που ελέγχουν τη μεταγραφή συγκεκριμένων γονιδίων. Μέσα λοιπόν από σύνθετα γονιδιακά ρυθμιστικά δίκτυα συμβαίνει η διαφοροποίηση των πρόδρομων κυττάρων προς ειδικούς τύπους κυττάρων με συγκεκριμένη μορφή και λειτουργία. Συχνά, μάλιστα, σχεδιάζονται πειραματικά πρωτόκολλα που μιμούνται τις διαδικασίες της διαφοροποίησης συγκεκριμένων κυτταρικών τύπων ξεκινώντας από βλαστοκύτταρα. Ιδιαίτερο ενδιαφέρον μεταξύ αυτών εμφανίζει η διαφοροποίηση κύτταρων που παράγουν ινσουλίνη, τα οποία θα μπορούσαν να αξιοποιηθούν για τη θεραπεία ευρέως διαδεδομένων ασθενειών όπως ο ινσουλινοεξαρτώμενος διαβήτης. Η αποκάλυψη, επομένως, των ρυθμιστικών δικτύων που κατευθύνουν τη διαφοροποίηση αυτών των κυττάρων υπόσχεται να βελτιώσει τα σχετικά πρωτόκολλα διαφοροποίησης αλλά και να συμβάλλει ενδεχομένως στην κατανόηση των μοριακών μηχανισμών της ασθένειας. Με τον όρο ρυθμιστικό δίκτυο υποδηλώνεται ένα σύνολο αλληλεπιδράσεων μεταξύ μεταγραφικών παραγόντων (trans-trans) αλλά και μεταξύ γονιδιακών περιοχών και πρωτεϊνών που ελέγχουν τη μεταγραφή (cis-trans). Στην παρούσα διατριβή, αντικείμενο εξέτασης αποτέλεσαν οι ρυθμιστικές αλληλουχίες ενός συνόλου γονιδίων τα οποία, με βάση πειραματικά δεδομένα διερεύνησης της γονιδιακής έκφρασης με μικροσυστοιχίες DNA, φαίνονταν να σχετίζονται με τη διαφοροποίηση των β-κυττάρων του παγκρέατος των θηλαστικών. Oι ρυθμιστικές αλληλουχίες συνήθως προσδένουν μεταγραφικούς παράγοντες και λόγω του σημαντικού τους ρόλου δέχονται έντονη εξελικτική πίεση. Δεδομένης λοιπόν της εξελικτικής συντήρησης των ρυθμιστικών στοιχείων, χρησιμοποιήθηκε η μέθοδος της συγκριτικής γονιδιωματικής με στόχο την ανεύρεση συντηρημένων ρυθμιστικών αλληλουχιών. Με σύγκριση ορθόλογων γονιδιωματικών περιοχών σε ένα σύνολο οργανισμών που περιλάμβανε ακόμη και τους πιο απομακρυσμένους φυλογενετικά οργανισμούς που διαθέτουν πάγκρεας, ταυτοποιήθηκαν συντηρημένες θέσεις πρόσδεσης μεταγραφικών παραγόντων. Πιο αναλυτικά, τα γονίδια που μελετήθηκαν επιλέχθηκαν με κριτήριο τη γνωστή ή πιθανή ρύθμισή τους από το μεταγραφικό παράγοντα NGN3, τον κύριο καθοδηγητή της διαφοροποίησης των ενδοκρινών κυττάρων του παγκρέατος. Με βάση πειραματικά δεδομένα ανάλυσης της γονιδιακής έκφρασης και με τη βοήθεια ισχυρών και ευέλικτων αλγορίθμων αναζητήθηκαν κοινά cis ρυθμιστικά στοιχεία στα ορθόλογα επαγόμενων και καταστελλόμενων από τον NGN3 γονιδίων. Παράλληλα αναζητήθηκαν διαφορές ανάμεσα στις ρυθμιστικές περιοχές των αντίστοιχων ομάδων γονιδίων. Αποκαλύφθηκε, έτσι, η ύπαρξη μιας συντηρημένης ρυθμιστικής περιοχής σε όλα τα ορθόλογα των οργανισμών που διαθέτουν πάγκρεας, η οποία περιλάμβανε θέσεις πρόσδεσης για μεταγραφικούς παράγοντες που εμπλέκονται στις διαδικασίες διαφοροποίησης των κυττάρων της ενδοκρινούς μοίρας του παγκρέατος. Η συγκεκριμένη περιοχή δεν εντοπίστηκε σε γονίδια των οποίων η έκφραση δεν σχετίζεται με την προηγούμενη διαδικασία, είτε αυτά εκφράζονται συνεχώς (B-ACTIN), είτε δεν εκφράζονται καθόλου στο πρώιμο έμβρυο (B-GLOBIN). Επιπλέον, η διερεύνηση εντοπισμού νουκλεοτιδικών προτύπων στις αλληλουχίες των ρυθμιστικών στοιχείων αποκάλυψε, επιπροσθέτως την παρουσία αλληλουχιών πρόσδεσης για τον παράγοντα AP4 μέσα στα ρυθμιστικά στοιχεία των καταστελλόμενων από τον NGN3 γονιδιών. Έγινε, έτσι, διάκριση των προαναφερθέντων ρυθμιστικών στοιχείων σε αυτά που φέρουν θέση πρόσδεσης για τον AP4 και σε αυτά που δεν φέρουν. Παράλληλα η ανάλυση ολόκληρου του γονιδιώματος του ποντικού και η στατιστική επεξεργασία των αποτελεσμάτων κατέδειξαν πως και τα δύο στοιχεία δεν εντοπίστηκαν τυχαία στα γονίδια που ελέγχονταν μεταγραφικά από τον NGN3. Δεδομένου ότι στην πλειοψηφία των γονιδίων που εξετάστηκαν οι ρυθμιστικές αυτές περιοχές εντοπίστηκαν μακριά από το σημείο έναρξης της μεταγραφής εκτιμήθηκε πως πρόκειται για ακολουθίες με ρόλο ενισχυτή, οι οποίες σε περίπτωση που μπορούν να προσδέσουν επιπροσθέτως και τον παράγοντα AP4 μετατρέπονται σε επιλεκτικούς καταστολείς των αντιστοίχων γονιδίων. Το τελευταίο συμπέρασμα υποστηρίχτηκε και από την ανάλυση της περιεκτικότητας των ρυθμιστικών στοιχείων σε GC που έδειξε ότι, όπως και οι περισσότερες αλληλουχίες με ρυθμιστικό ρόλο κατά την εμβρυογένεση, έτσι και οι συγκεκριμένες ήταν πτωχές σε GC, κάτι όμως που γενικότερα δεν ισχύει για τους υποκινητές. Η διερεύνηση των αλληλεπιδράσεων των πρωτεϊνών-μεταγραφικών παραγόντων (trans-trans) που κατά πρόβλεψη προσδένονται στα συντηρημένα ρυθμιστικά στοιχεία αποκάλυψε την ύπαρξη ενός συμπλόκου από γενικούς και ειδικούς μεταγραφικούς παράγοντες. Το σύμπλοκο αυτό συνδεόμενο με ειδικούς μεταγραφικούς ρυθμιστές μπορεί να λειτουργεί άλλοτε ως επαγωγέας και άλλοτε ως καταστολέας της μεταγραφής συγκεκριμένων γονιδίων. Σημαντικό ρόλο στη διαφορική λειτουργία του συγκεκριμένου συμπλόκου θεωρήθηκε ότι διαδραματίζει η αλλαγή των επιπέδων ακετυλίωσης της χρωματίνης λόγω της παρουσίας ακετυλασών και αποακετυλασών στο σύμπλοκο. Οι αλληλεπιδράσεις μεταξύ των πρωτεϊνών (trans-trans), μαζί με τις αλληλεπιδράσεις μεταξύ των γονιδίων που αναλύθηκαν (cis-cis) αλλά και οι συνδυασμοί αυτών ενσωματώθηκαν σε ένα ευρύτερο ρυθμιστικό δίκτυο με κεντρικό ρυθμιστή τον NGN3. Προέκυψε, λοιπόν, ένα ρυθμιστικό δίκτυο, από το οποίο υποδεικνύεται ότι με επιλεκτική επαγωγή συγκεκριμένων γονιδίων και με καταστολή άλλων επιτυγχάνεται τελικά η διαφοροποίηση κυττάρων ικανών να παράγουν ινσουλίνη. / Mammalian development occurs by the progressive determination of cells from a pluripotent undifferentiated state through successive states of gradually restricted developmental potential, until the full complement of mature terminally differentiated cells has been specified. Embryonic development is a complex and highly orchestrated process during which multiple cell movements and changes in gene expression must be spatially and temporally coordinated to ensure that embryogenesis proceeds correctly. Complex genetic regulatory networks receive input in the form of extracellular signals and output instructions on the regulated expression of specific genes. The linchpins of the regulatory networks are the cis-regulatory elements that directly control gene expression through interpretation of the tissue-specific transcription factors (trans-elements). Embryonic stem cells are orientated across the dorso-ventral and the anterior-posterior axis of the early embryo. The orientation of progenitor cells along these two axes is thought to influence their fate by defining the identity and concentration of inductive signals to which they are exposed. In an effort to develop cell-based therapies, (i.e. for diabetes) experimental protocols aim to mimic the biological procedures that take place during embryonic development in order to differentiate embryonic stem cells towards specific cell types. One of the foremost challenges towards the development of cell therapies for diabetic people is to achieve the directed differentiation of cells capable of producing insulin. Elucidation of the genetic networks involved in the endocrine pancreas specification are thought to be essential for devising rational protocols to efficiently differentiate embryonic stem cells or pancreas progenitor cells into fully differentiated endocrine subtypes. Computational approaches allow the unravelling of complex regulatory networks including genomic (cis-cis) or proteomic (trans-trans) interactions or a combination (cis-trans) of both. In this study the genomic regulatory regions (cis elements) of several genes known and putative targets of the transcription factor NGN3 were analyzed. The NGN3 transcription factor is the major regulator of “insulin-producing cell” formation. Taking into account data from microarray experiments from pancreas progenitor cells, in which NGN3 has been induced, genes shown to be co-regulated (upregulated or downregulated) by this transcription factor were selected for analysis. Using a combination of sophisticated computational tools for exploiting and analyzing genomic data and developing the suitable algorithms, an extensive in silico analysis of the regulatory regions of these genes was performed. Evolutionarily conserved regions are linked with experimentally identified regulatory elements. Comparative genomics are commonly used in order to identify transcription factor binding sites, which are functionally important regions that are thought to be well-conserved. Analysis of genomic regulatory regions included not only genes corregulated by NGN3, but also their orthologs in several species including the most phylogenetically distant species (fish), which have pancreas. In parallel, housekeeping genes, like B-ACTIN, and those not expressed in embryos and stem cells, like B-GLOBIN, were used as negative controls. Regulatory region analysis revealed the presence of a highly conserved regulatory element, where many transcription factors with established involvement in pancreas development bind, in all the orthologs of several genes co-regulated by NGN3. Furthermore, motif identification in separate clusters of the regulatory elements of either upregulated or downregulated genes revealed the presence of additional binding motifs for the factor AP4 only in downregulated genes. In parallel, the regulatory region analysis of the entire mouse genome and the statistical analysis of the upcoming results showed that both types of regulatory elements (with and without AP4) were non-randomly identified inside the regulatory regions of genes whose transcription is controlled by NGN3. Moreover the selective presence of the AP4 binding sequence into this region renders it a highly specific suppressor found in only a small number of genes downregulated by NGN3. Taking into account that both these regulatory elements were identified at considerable distances from each gene’s transcription start site, it was assumed that they represent enhancers, and those capable of binding AP4 were considered silencers. This conclusion was enforced by the compositional analysis of these regions showing low GC levels, similarly to the majority of the regulatory regions implicated in embryonic development, something that has not been reported for promoter sequences. Moreover, analysis of protein-protein interactions showed that some of the transcription factors, predicted to bind onto these elements, together with other non-specific transcription factors, constitute a core transcription control complex. This protein complex interacts with the remaining members of the predicted cluster of transcription regulators and works either as an inducer or a suppressor of transcription. This is determined by the presence of a HAT and/or an HDAC in this protein complex assumed to locally control chromatin acetylation. Based on these data, we constructed a model of the complex regulatory network that describes how through the transcriptional regulation of the analyzed genes mainly guided by ΝGN3 the gradual differentiation of cells capable of producing insulin takes place.

abstract: A central task for historians and philosophers of science is to characterize and analyze the epistemic practices in a given science. The epistemic practice of a science includes its explanatory goals as well as the methods used to achieve these goals. This dissertation addresses the epistemic practices in gene expression research spanning the mid-twentieth century to the twenty-first century. The critical evaluation of the standard historical narratives of the molecular life sciences clarifies certain philosophical problems with respect to reduction, emergence, and representation, and offers new ways with which to think about the development of scientific research and the nature of scientific change. The first chapter revisits some of the key experiments that contributed to the development of the repression model of genetic regulation in the lac operon and concludes that the early research on gene expression and genetic regulation depict an iterative and integrative process, which was neither reductionist nor holist. In doing so, it challenges a common application of a conceptual framework in the history of biology and offers an alternative framework. The second chapter argues that the concept of emergence in the history and philosophy of biology is too ambiguous to account for the current research in post-genomic molecular biology and it is often erroneously used to argue against some reductionist theses. The third chapter investigates the use of network representations of gene expression in developmental evolution research and takes up some of the conceptual and methodological problems it has generated. The concluding comments present potential avenues for future research arising from each substantial chapter. In sum, this dissertation argues that the epistemic practices of gene expression research are an iterative and integrative process, which produces theoretical representations of the complex interactions in gene expression as networks. Moreover, conceptualizing these interactions as networks constrains empirical research strategies by the limited number of ways in which gene expression can be controlled through general rules of network interactions. Making these strategies explicit helps to clarify how they can explain the dynamic and adaptive features of genomes. / Dissertation/Thesis / Doctoral Dissertation Philosophy 2016

abstract: As we migrate into an era of personalized medicine, understanding how bio-molecules interact with one another to form cellular systems is one of the key focus areas of systems biology. Several challenges such as the dynamic nature of cellular systems, uncertainty due to environmental influences, and the heterogeneity between individual patients render this a difficult task. In the last decade, several algorithms have been proposed to elucidate cellular systems from data, resulting in numerous data-driven hypotheses. However, due to the large number of variables involved in the process, many of which are unknown or not measurable, such computational approaches often lead to a high proportion of false positives. This renders interpretation of the data-driven hypotheses extremely difficult. Consequently, a dismal proportion of these hypotheses are subject to further experimental validation, eventually limiting their potential to augment existing biological knowledge. This dissertation develops a framework of computational methods for the analysis of such data-driven hypotheses leveraging existing biological knowledge. Specifically, I show how biological knowledge can be mapped onto these hypotheses and subsequently augmented through novel hypotheses. Biological hypotheses are learnt in three levels of abstraction -- individual interactions, functional modules and relationships between pathways, corresponding to three complementary aspects of biological systems. The computational methods developed in this dissertation are applied to high throughput cancer data, resulting in novel hypotheses with potentially significant biological impact. / Dissertation/Thesis / Ph.D. Computer Science 2012

abstract: Biological systems are complex in many dimensions as endless transportation and communication networks all function simultaneously. Our ability to intervene within both healthy and diseased systems is tied directly to our ability to understand and model core functionality. The progress in increasingly accurate and thorough high-throughput measurement technologies has provided a deluge of data from which we may attempt to infer a representation of the true genetic regulatory system. A gene regulatory network model, if accurate enough, may allow us to perform hypothesis testing in the form of computational experiments. Of great importance to modeling accuracy is the acknowledgment of biological contexts within the models -- i.e. recognizing the heterogeneous nature of the true biological system and the data it generates. This marriage of engineering, mathematics and computer science with systems biology creates a cycle of progress between computer simulation and lab experimentation, rapidly translating interventions and treatments for patients from the bench to the bedside. This dissertation will first discuss the landscape for modeling the biological system, explore the identification of targets for intervention in Boolean network models of biological interactions, and explore context specificity both in new graphical depictions of models embodying context-specific genomic regulation and in novel analysis approaches designed to reveal embedded contextual information. Overall, the dissertation will explore a spectrum of biological modeling with a goal towards therapeutic intervention, with both formal and informal notions of biological context, in such a way that will enable future work to have an even greater impact in terms of direct patient benefit on an individualized level. / Dissertation/Thesis / Ph.D. Computer Science 2013

Orientador: Renato Vicentini dos Santos / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-23T02:31:35Z (GMT). No. of bitstreams: 1 Murad_NataliaFaraj_M.pdf: 15243579 bytes, checksum: a5e50145fbdf4bddfb2ea99313b17991 (MD5) Previous issue date: 2013 / Resumo: A cana-de-açúcar é uma das mais importantes plantas cultivadas no Brasil, que é o maior produtor e exportador mundial. Seu valor econômico é devido principalmente a sua capacidade de estocar sacarose nos colmos. Os padrões de expressão gênica podem regular processos de desenvolvimento da planta e influenciar no acúmulo de sacarose em tecidos de reserva. A regulação desses padrões ocorre através de complexos sistemas de interações entre muitos genes e seus produtos, resultando em uma complexa rede de regulação gênica. Modelos gráficos probabilísticos têm sido amplamente utilizados para inferência e representação dessas redes. Dentre eles, as redes bayesianas são o principal por ser considerado o método mais flexível e também requererem um número reduzido de parâmetros para a descrição do modelo. Sendo assim, este estudo utilizou a metodologia de redes bayesianas para inferência de interações regulatórias entre genes de metabolismo e sinalização de sacarose a partir de dados de expressão gênica, obtidos através de microarrays, disponíveis no Gene Expression Omnibus (GEO). As redes foram obtidas através de softwares para inferência de redes e então analisadas quanto aos genes que as compõem e padrões de expressão. Os genes foram agrupados em clusters considerando-se seus padrões de coexpressão. Os genes mais representados no cluster da enzima sacarose fosfato sintase (SPS) em cana são genes de relacionados à tradução, ligação ao DNA e genes de função desconhecida, enquanto os menos representados são de fotossíntese, resposta a hormônios, e outros eventos metabólicos. A rede do cluster da SPS apresentou sete genes principais (hubs) que aparentam ter um importante papel dentro do cluster. Foi obtida também uma rede considerando genes selecionados em estudos com experimentos de microarrays previamente publicados. Uma dessas redes possui 136 genes e apresentou 6 genes principais, sendo que a maioria deles é de fotossíntese. Na rede considerando genes diferencialmente expressos nesses experimentos (265 genes), genes que pertencem à mesma categoria funcional tenderam a sofrer regulação por um único gene em comum, formando grupos de funções semelhantes em cada hub / Abstract: Sugarcane is one of the most important plants cultivated in Brazil which is the world's largest producer and exporter. Its economic yield is mainly due to its high sucrose content. The patterns of gene expression may regulate processes of plant development and influence the accumulation of sucrose by storage tissues. The regulation of these patterns occurs through complex systems of interactions between many genes and their products, resulting in a complex gene regulatory network. Probabilistic graphical models have been widely used for inference and representation of these networks. Among them, Bayesian networks are the main for being considered to be the most flexible method and also requiring a reduced number of parameters to the model description. Then, this work has used the Bayesian network methodology for inference of regulatory interactions between signaling and sucrose metabolism genes from gene expression data, obtained from microarrays, available on Gene Expression Omnibus (GEO). Networks were generated by networks inference softwares, and then analyzed observing their composing genes and expression patterns. The genes were grouped considering their coexpression patterns. The most represented genes in the sacarose phosphate syntase (SPS) cluster are related with translation, DNA biding and unknown function genes while the least represented are of photosynthesis, hormone response and other metabolic events. The SPS cluster network presented 7 main hubs that seem to play an important role in the cluster. It was also obtained a network considering genes selected from studies with microarray experiments previously published. One of these gene networks has 136 genes and it presented 6 main genes, being the most of them are from photosynthesis. In the network considering differential expressed in this experiments, genes that are from the same functional category tended to suffer regulation for one unique common gene, forming groups of genes with similar function on each hub / Mestrado / Genetica Vegetal e Melhoramento / Mestra em Genética e Biologia Molecular

The specification and modulation of cell-type specific dendritic morphologies plays a pivotal role in nervous system development, connectivity, structural plasticity, and function. Regulation of gene expression is controlled by a wide variety of cellular and molecular mechanisms, of which two major types are transcription factors (TFs) and microRNAs (miRNAs). In Drosophila, dendritic complexity of dendritic arborization (da) sensory neurons of the peripheral nervous system are known to be regulated by two transcription factors Cut and Knot, although much remains unknown about the molecular mechanisms and regulatory networks via which they regulate the final arbor shape through spatio-temporal modulation of dendritic development and dynamics. Here we use bioinformatics analysis of transcriptomic data to identify putative genomic targets of these TFs with a particular emphasis on those that effect neuronal cytoskeletal architecture. We use transcriptomic, as well as data from various genomic and protein interaction databases, to build a weighted functional gene regulatory network for Knot, to identify the biological pathways and downstream genes that this TF regulates. To corroborate bioinformatics network predictions, knot putative targets, which classify into neuronal and cytoskeletal functional groups, have been experimentally validated by in vivo genetic perturbations to elucidate their role in Knot-mediated Class IV (CIV) dendritogenesis. MicroRNAs (miRNAs) have emerged as key post-transcriptional regulators of gene expression, however identification of biologically-relevant target genes for this epigenetic regulatory mechanism remains a significant challenge. To address this knowledge gap, we have developed a novel R based tool, IntramiR-ExploreR, that facilitates integrated discovery of miRNA targets by incorporating target databases and novel target prediction algorithm to arrive at high confidence intragenic miRNA target predictions. We have explored the efficacy of this tool using D.melanogaster as a model organism for bioinformatics analyses and functional validation, and identified targets for 83 intragenic miRNAs. Predicted targets were validated, using in vivo genetic perturbation. Moreover, we are constructing interaction maps of intragenic miRNAs focusing on neural tissues to uncover regulatory codes via which these molecules regulate gene expression to direct cellular development.