Tesi sul tema "Genetic regulatory networks"
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1
Bokes, Pavol. "Genetic regulatory networks". Thesis, University of Nottingham, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523016.
Testo completo
2
Abul, Osman. "Controlling Discrete Genetic Regulatory Networks". Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605739/index.pdf.
Testo completoAbstract (sommario):
Genetic regulatory networks can model dynamics of cells. They also allow for studying the effect of internal or external interventions. Selectively applying interventions towards a certain objective is known as controlling network dynamics. In this thesis work, the issue of how the external interventions af
fect the network is studied. The effects are determined using differential gene expression analysis. The differential gene expression problem is further studied to improve the power of the given method. Control problem for dynamic discrete regulatory networks is formulated. This also addresses the needs for various control strategies, e.g., finite horizon, infinite horizon, and various accounting of state and intervention costs. Control schemes for small to large
networks are proposed and experimented. A case study is provided to show how the proposals are exploitedalso given is the need for and effectiveness of various control schemes.
3
Xiao, Yufei. "Boolean models for genetic regulatory networks". [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1498.
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4
Pal, Ranadip. "Discovering relationships in genetic regulatory networks". Thesis, Texas A&M University, 2004. http://hdl.handle.net/1969.1/1230.
Testo completoAbstract (sommario):
The development of cDNA microarray technology has made it possible to simultaneously monitor the expression status of thousands of genes. A natural use for this vast amount of information would be to try and figure out inter-gene relationships by studying the gene expression patterns across different experimental conditions and to build Gene Regulatory Networks from these data. In this thesis, we study some of the issues involved in Genetic Regulatory Networks. One of them is to discover and elucidate multivariate logical predictive relations among gene expressions and to demonstrate how these logical relations based on coarse quantization closely reflect corresponding relations in the continuous data. The other issue involves construction of synthetic Probabilistic Boolean Networks with particular attractor structures. These synthetic networks help in testing of various algorithms like Bayesian Connectivity based approach for design of Probabilistic Boolean Networks.
5
Pal, Ranadip. "Modeling and control of genetic regulatory networks". Thesis, [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1494.
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6
Parmar, Kiresh. "Time-delayed models of genetic regulatory networks". Thesis, University of Sussex, 2017. http://sro.sussex.ac.uk/id/eprint/70716/.
Testo completoAbstract (sommario):
In this thesis I have analysed several mathematical models, which represent the dynamics of genetic regulatory networks. Methods of bifurcation analysis and direct numerical simulations were employed to study the biological phenomena that can occur due to the presence of time delays, such as stable periodic oscillations induced by Hopf bifurcations. To highlight the biological implications of time-delayed systems, different models of genetic regulatory networks as relevant to the onset and development of cancer were studied in detail, as well as genetic regulatory networks which describe the effects of transcription factors in the immune system. A network of an oscillator coupled with a switch was explored, as systems such as these are prevalent in genetic regulatory networks. The effects of time delays on its oscillatory and bistable behaviour were then investigated, the results of which were compared with available results from the literature.
7
Zhao, Dacheng. "Representation and visualization of genetic regulatory networks". Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/42131.
Testo completoAbstract (sommario):
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 61-65).
We present a new framework, Sonnet, for the interactive visualization of large, complex biological models that are represented as graphs. Sonnet provides a flexible representation framework and graphical user interface for filtering and layout, allowing users to rapidly visualize different aspects of a data set. Many previous approaches have required users to write customized software in order to achieve the same functionality. With Sonnet, once features of interest are identified, they can be captured as figures for offline presentation. We demonstrate the application of Sonnet to the visualization and manipulation of transcriptional regulatory networks in yeast. Sonnet is particularly well adapted to this application as native presentation of these networks yields dense and difficult to decipher results.
by Dacheng Zhao.
M.Eng.
8
Zhang, Shuqin. "Mathematical models and algorithms for genetic regulatory networks". Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38842828.
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9
Zhang, Shuqin, e 張淑芹. "Mathematical models and algorithms for genetic regulatory networks". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38842828.
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10
Santos, Bruno Acácio de Castro Moreira dos. "Small RNAs in gene regulatory networks". Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708543.
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11
Li, Jiewei, e 李劼伟. "Stability analysis of uncertain genetic regulatory newtworks". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50899788.
Testo completoAbstract (sommario):
Genetic regulatory network (GRN) is a fundamental research area in systems biology.
This thesis studies the stability of a class of GRN models. First, a condition is proposed to ensure the robust stability of uncertain GRNs with SUM regulatory functions. It is assumed that the uncertainties are in the form of a parameter vector that determines the coefficients of the model via given functions. Then, the global asymptotic stability conditions of uncertain GRNs affected by disturbances and time delays are further explored. The conditions are obtained by solving a convex optimization problem by exploring the sum of squares (SOS) of matrix polynomials and by introducing polynomially parameter-dependent Lyapunov-Krasovskii functionals (LKFs). Moreover, based on the uncertain GRNs with guaranteed disturbance attenuation, it is shown that estimates of the sought stable uncertainty sets can be obtained through a recursive strategy based on parameter-dependent Lyapunov functions and the SOS.
Second, the stability conditions of GRNs described by piecewise models are considered. Depending on whether the state partitions and mode transitions are known or unknown as priori, the proposed networks are divided into two categories, i.e., switched GRNs and hybrid GRNs. It is shown that, by using common polynomial Lyapunov functions and piecewise polynomial Lyapunov functions, two conditions are established to ensure the global asymptotic stability for switched and hybrid GRNs, respectively. In addition, it is shown that, by using the SOS techniques, stability conditions in the form of LMIs for both models can be obtained.
Third, the multi-stability of uncertain GRNs with multivariable regulation functions is investigated. It is shown that, by using the Lyapunov functional method and LMI technology, a criterion is established to ensure the robust asymptotical stability of the uncertain GRNs, and such condition can be extended to deal with the multi-stability problem. Moreover, it is shown that by using the square matrix representation (SMR) and by adopting polynomially parameter-dependent Lyapunov functions, a condition in the form of LMIs for robust stability for all admissible uncertainties can be obtained.
Examples with synthetic and real biological models are presented in each section to illustrate the applicability and effectiveness of the theoretical results.published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
12
Weiss, Ron 1970. "Cellular computation and communications using engineered genetic regulatory networks". Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8228.
Testo completoAbstract (sommario):
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 130-138).
In this thesis, I present an engineering discipline for obtaining complex, predictable, and reliable cell behaviors by embedding biochemical logic circuits and programmed intercellular communications into cells. To accomplish this goal, I provide a well-characterized component library, a biocircuit design methodology, and software design tools. I have built and characterized an initial cellular gate library with biochemical gates that implement the NOT, IMPLIES, and AND logic functions in E. coli cells. The logic gates perform computation using DNA-binding proteins, small molecules that interact with these proteins, and segments of DNA that regulate the expression of the proteins. I introduce genetic process engineering, a methodology for modifying the DNA encoding of existing genetic elements to achieve the desired input/output behavior for constructing reliable circuits of significant complexity. I demonstrate the feasibility of digital computation in cells by building several operational in-vivo digital logic circuits, each composed of three gates that have been optimized by genetic process engineering.
(cont.) I also demonstrate engineered intercellular communications with programmed enzymatic activity and chemical diffusions to carry messages, using DNA from the Vibrio fischeri lux operon. The programmed communications is essential for obtaining coordinated behavior from cell aggregates. In addition to the above experimental contributions, I have developed BioSPICE, a prototype software tool for biocircuit design. It supports both static and dynamic simulations and analysis of single cell environments and small cell aggregates. Finally, I present the Microbial Colony Language (MCL), a model for programming cell aggregates. The language is expressive enough for interesting applications, yet relies on simple primitives that can be mapped to the engineered biological processes described above.
by Ron Weiss.
Ph.D.
13
Wang, Yifei. "Evolutionary innovations and dynamics in Wagner's model of Genetic Regulatory Networks". Thesis, University of Bath, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.687330.
Testo completoAbstract (sommario):
The gene regulatory network (GRN) controls the expression of genes providing phenotypic traits in living organisms. In particular, transcriptional regulation is essential to life, as it governs all levels of gene products that enable cell survival and numerous cellular functions. However, there is still poor understanding of how shifts in gene regulation alter the underlying evolutionary dynamics and consequently generate evolutionary innovations. By employing Wagner's GRN model, this dissertation investigates how the interplay of simple evolutionary forces (mutation and recombination) with natural selection acting on gene regulatory dynamics can generate major evolutionary innovations. In this dissertation, firstly, I review all currently available research papers using Wagner's GRN model, which is also employed as the computational model used extensively in the remaining chapters. I then describe how Wagner's GRN model and its variants are implemented. Finally, network properties such as stability, robustness and path length in initial populations are investigated. In the first study, I explore the characteristics of compensatory mutation in the context of genetic networks. Specifically, I find that 1) compensatory mutations are relatively insensitive to the size and connectivity of the network, 2) compensatory mutations are more likely to occur in genes at or adjacent to the site of a previous deleterious mutation and 3) compensatory mutations are more likely to be driven by mutations with a relatively large regulatory impact. In the second study, I further investigate the evolutionary consequences of the properties of compensatory mutation discovered previously. Specifically, I find that 1) compensatory mutations can occur regardless of patterns of selection, 2) networks with compensatory mutations exhibit proportionately higher robustness when compensatory mutations interact closely with deleterious mutations or have large effects on gene regulation, and 3) regulatory complexity can arise as a consequence of the propensity for co-localised and large-effect compensatory mutations. In the third study, I provide a mechanistic understanding of how recombination benefits sexual lineages. Specifically, I find that 1) recombination together with selection for developmental stability can drive populations towards the optimum, 2) recombination does not frequently disrupt well-adapted lineages as conventionally expected, and 3) recombination facilitates finding good genetic combinations which are robust to disruption, although it also rapidly purges weaker configurations. In the final study, I show that the selection pressure acting on rewiring gene regulation is critical to increasing benefits for sexual lineages whilst mitigating costs of sex and recombination. Specifically, I find that 1) strong selection strength can greatly benefit low-fitness sexual lineages, especially at the early stage, 2) recombination is initially costly, but it can rapidly evolve to compensate for costs of sex and recombination, and 3) sexual lineages with low levels of sex and recombination can outcompete strictly asexual populations under higher selection pressure and lower mutation rates. The results presented for all of the studies are important for mechanistically understanding evolutionary innovations through altering transcriptional regulatory dynamics. These innovations include 1) facilitating alternative pathway evolution, 2) driving regulatory complexity, 3) benefiting sexual reproduction, and 4) resisting invasion against asexual lineages.
14
Banks, Richard A. "Qualitatively modelling genetic regulatory networks : Petri net techniques and tools". Thesis, University of Newcastle Upon Tyne, 2009. http://hdl.handle.net/10443/2108.
Testo completoAbstract (sommario):
The development of post-genomic technologies has led to a paradigm shift in the way we study genetic regulatory networks (GRNs) - the underlying systems which mediate cell function. To complement this, the focus is on devising scalable, unambiguous and automated formal techniques for holistically modelling and analysing these complex systems. Quantitative approaches offer one possible solution, but do not appear to be commensurate with currently available data. This motivates qualitative approaches such as Boolean networks (BNs) , which abstractly model the system without requiring such a high level of data completeness. Qualitative approaches enable fundamental dynamical properties to be studied, and are well-suited to initial investigations. However, strengthened formal techniques and tool support are required if they are to meet the demands of the biological community. This thesis aims to investigate, develop and evaluate the application of Petri nets (PNs) for qualitatively modelling and analysing GRNs. PNs are well-established in the field of computer science, and enjoy a number of attractive benefits, such a wide range of techniques and tools, which make them ideal for studying biological systems. We take an existing qualitative PN approach for modelling GRNs based on BNs, and extend it to more general models based on multi-valued networks (MVNs). Importantly, we develop tool support to automate model construction. We illustrate our approach with two detailed case studies on Boolean models for carbon stress in Escherichia coli and sporulation in Bacillus subtilis, and then consider a multi-valued model of the former. These case studies explore the analysis power of PN s by exploiting a range of techniques and tools. A number of behavioural differences are identified between the two E. coli models which lead us to question their formal relationship. We investigate this by proposing a framework for reasoning about the behaviour of MVNs at different levels of abstraction. We develop tool support for practical models, and show a number of important results which motivate the need for multi-valued modelling. Asynchronous BN s can be seen to be more biologically realistic than their synchronous counterparts. However, they have the drawback of capturing behaviour which is unrealisable in practice. We propose a novel approach for refining such behaviour using signal transition graphs, a PN formalism from asynchronous circuit design. We automate our approach, and demonstrate it using a BN of the lysis-lysogeny switch in phage A. Our results show that a more realistic asynchronous model can be derived which preserves the stochastic switch.
15
Giagos, Vasileios. "Inference for auto-regulatory genetic networks using diffusion process approximations". Thesis, Lancaster University, 2010. http://eprints.lancs.ac.uk/68421/.
Testo completoAbstract (sommario):
The scope of this thesis is to propose new inferential tools, based on diffusion process approximations, for the study of the kinetic parameters in auto-regulatory networks. In the first part of this thesis, we study the applicability of the EA methodology to Stochastic Differential Equations (SDEs) which approximate biological systems. In principle EA can be applied to any scalar-valued SDE as long as a transformation (known as Lamperti transform) exists that sets the (new) infinitesimal variance to unity. We explore the numerical limitations of this requirement by considering a biological system that can be expressed as a scalar non-linear SDE. Next, we consider the multidimensional extension of this transformation and we show, with a counterexample, that EA can be applied to a class of SDEs which is wider than the class of reducible diffusions. In the second part of this thesis, we proposed a reparametrization of the kinetic constants that leads to an approximation known as the Linear Noise approximation (LNA). We prove that LNA converges to a linear SDE, as the size of the biological system increases. Since the LNA is a linear SDE, it has a known transition density with parameters given as the solutions of a system of Ordinary Differential Equations (ODEs) which are usually obtained numerically. Furthermore, we compare the LNA's simulation performance to the performance of other (approximate and exact) methods under different modelling scenarios and we relate the performance of the approximate methods to the system size. In addition, we consider LNA as an inferential tool and we use two methods, the Restarting (RE), which we propose, and the Non-Restarting (NR) method, proposed by Komorowski et. al. (2009) to derive the LNA's likelihood. The two methods differ on the initial conditions that they pose in order to solve the underlying ODEs. We compare the performance of the two methods by considering data generated under different scenarios. Finally, we discuss the lnar, a package for the R statistical environment, that we developed to implement the LNA methodology.
16
Hartemink, Alexander J. (Alexander John) 1972. "Principled computational methods for the validation discovery of genetic regulatory networks". Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8699.
Testo completoAbstract (sommario):
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 193-206).
As molecular biology continues to evolve in the direction of high-throughput collection of data, it has become increasingly necessary to develop computational methods for analyzing observed data that are at once both sophisticated enough to capture essential features of biological phenomena and at the same time approachable in terms of their application. We demonstrate how graphical models, and Bayesian networks in particular, can be used to model genetic regulatory networks. These methods are well-suited to this problem owing to their ability to model more than pair-wise relationships between variables, their ability to guard against over-fitting, and their robustness in the face of noisy data. Moreover, Bayesian network models can be scored in a principled manner in the presence of both genomic expression and location data. We develop methods for extending Bayesian network semantics to include edge annotations that allow us to model statistical dependencies between biological factors with greater refinement. We derive principled methods for scoring these annotated Bayesian networks. Using these models in the presence of genomic expression data requires suitable methods for the normalization and discretization of this data.
(cont.) We present novel methods appropriate to this context for performing each of these operations. With these elements in place, we are able to apply our scoring framework to both validate models of regulatory networks in comparison with one another and discover networks using heuristic search methods. To demonstrate the utility of this framework for the elucidation of genetic regulatory networks, we apply these methods in the context of the well-understood galactose regulatory system and the less well-understood pheromone response system in yeast. We demonstrate how genomic expression and location data can be combined in a principled manner to enable the induction of models not readily discovered if the data sources are considered in isolation.
by Alexander John Hartemink.
Ph.D.
17
Moser, Felix Ph D. Massachusetts Institute of Technology. "Engineered sensors and genetic regulatory networks for control of cellular metabolism". Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/86286.
Testo completoAbstract (sommario):
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2013.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 114-125).
Complex synthetic genetic programs promise unprecedented control over cellular metabolism and behavior. In this thesis, I describe the design and development of a synthetic genetic program to detect conditions underlying acetate formation in Escherichia coli. To construct this program, I first developed sensors that detected and propagated relevant information into genetic circuits. These sensors include a novel sensor for genotoxic methylation exposure in Saccharomyces cerevisiae and sensors for oxygen, acetate, and glycolytic flux in E. coli. The methylation sensor served to prototype generalizable tuning mechanisms and was tuned to a sensitivity and detection threshold useful for several applications, including the detection of Mel formation in methyl halide transferase-expressing cultures of yeast and the detection of Mel in soil. The sensors for oxygen and acetate were integrated into a program that can uniquely detect acetate formation in anaerobic conditions in E. coli. Finally, to validate their use at higher scales in production strains, the oxygen sensor and two genetic programs were characterized in 10 L fed-batch fermentations. Together, this work demonstrates the characterization of novel genetic elements, their integration into genetic programs, and the validation of those programs at industrially relevant scales.
by Felix Moser.
Ph. D.
18
Nicol, Megan E. "Unraveling the Nexus: Investigating the Regulatory Genetic Networks of Hereditary Ataxias". Ohio University Honors Tutorial College / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1400604580.
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19
Thomas, Rodney H. "Machine Learning for Exploring State Space Structure in Genetic Regulatory Networks". Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1053.
Testo completoAbstract (sommario):
Genetic regulatory networks (GRN) offer a useful model for clinical biology. Specifically, such networks capture interactions among genes, proteins, and other metabolic factors. Unfortunately, it is difficult to understand and predict the behavior of networks that are of realistic size and complexity. In this dissertation, behavior refers to the trajectory of a state, through a series of state transitions over time, to an attractor in the network. This project assumes asynchronous Boolean networks, implying that a state may transition to more than one attractor. The goal of this project is to efficiently identify a network's set of attractors and to predict the likelihood with which an arbitrary state leads to each of the network’s attractors. These probabilities will be represented using a fuzzy membership vector.
Predicting fuzzy membership vectors using machine learning techniques may address the intractability posed by networks of realistic size and complexity. Modeling and simulation can be used to provide the necessary training sets for machine learning methods to predict fuzzy membership vectors. The experiments comprise several GRNs, each represented by a set of output classes. These classes consist of thresholds τ and ¬τ, where τ = [τlaw,τhigh]; state s belongs to class τ if the probability of its transitioning to attractor belongs to the range [τlaw,τhigh]; otherwise it belongs to class ¬τ. Finally, each machine learning classifier was trained with the training sets that was previously collected. The objective is to explore methods to discover patterns for meaningful classification of states in realistically complex regulatory networks.
The research design took a GRN and a machine learning method as input and produced output class < Ατ > and its negation ¬ < Ατ >. For each GRN, attractors were identified, data was collected by sampling each state to create fuzzy membership vectors, and machine learning methods were trained to predict whether a state is in a healthy attractor or not. For T-LGL, SVMs had the highest accuracy in predictions (between 93.6% and 96.9%) and precision (between 94.59% and 97.87%). However, naive Bayesian classifiers had the highest recall (between 94.71% and 97.78%). This study showed that all experiments have extreme significance with pvalue < 0.0001. The contribution this research offers helps clinical biologist to submit genetic states to get an initial result on their outcomes. For future work, this implementation could use other machine learning classifiers such as xgboost or deep learning methods. Other suggestions offered are developing methods that improves the performance of state transition that allow for larger training sets to be sampled.
20
Ramos, Rodríguez Mireia. "β-cells cis-regulatory networks and type 1 diabetes". Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/672192.
Testo completoAbstract (sommario):
Type 1 Diabetes (T1D) is a celltargeted autoimmune disease, leading to a reduction in pancreatic cell mass that renders patients insulindependent for life. In early stages of the disease, cells from the immune system infiltrate pancreatic islets in a process called insulitis. During this stage, a crosstalk is established between cells in the pancreatic islets and the infiltrating immune cells, mediated by the release of cytokines and chemokines. Studying the gene regulatory networks driving cell responses during insulitis, will allow us to pinpoint key gene pathways leading to cell lossoffunction and apoptosis, and also to understand the role cells have in their own demise. In the present thesis, we used two different cytokine cocktails, IFN and IFN + IL1, to model early and late insulitis, respectively. After exposing cells and pancreatic islets to such proinflammatory cytokines, we characterized the changes in their chromatin landscape, gene networks and protein profiles. Using both models, we observed dramatic chromatin remodeling in terms of accessibility and/or H3K27ac histone modification enrichment, coupled with upregulation of the nearby genes and increased abundance of the corresponding protein. Mining gene regulatory networks of cells exposed to IFN revealed two potential therapeutic interventions which were able to reduce interferon signature in cells: 1) Inhibition of bromodomain proteins, which resulted in a downregulation of IFNinduced HLAI and CXCL10 expression; 2) Baricitnib, a JAK1/2 inhibitor, which was able to reduce both IFNinduced HLAI and CXCL10 expression levels and cell apoptosis. In
cells exposed to IFN + IL1, we were able to identify a subset of novel
regulatory elements uncovered upon the exposure, which we named Induced Regulatory Elements (IREs). Such regions were enriched for T1Dassociated risk variants, suggesting that cells might carry a portion of T1D genetic risk. Interestingly, we identified two T1D lead variants overlapping IREs, in which the risk allele modulated the IRE enhancer activity, exposing a potential T1D mechanism acting through cells. To facilitate the access to these genomic data, together with other datasets relevant for the pancreatic islet community, we developed the Islet Regulome Browser (http://www.isletregulome.org/), a free web application that allows exploration and integration of pancreatic islet genomic data.
21
Arda, H. Efsun. "C. Elegans Metabolic Gene Regulatory Networks: A Dissertation". eScholarship@UMMS, 2010. https://escholarship.umassmed.edu/gsbs_diss/479.
Testo completoAbstract (sommario):
In multicellular organisms, determining when and where genes will be expressed is critical for their development and physiology. Transcription factors (TFs) are major specifiers of differential gene expression. By establishing physical contacts with the regulatory elements of their target genes, TFs often determine whether the target genes will be expressed or not. These physical and/or regulatory TF-DNA interactions can be modeled into gene regulatory networks (GRNs), which provide a systems-level view of differential gene expression. Thus far, much of the GRN delineation efforts focused on metazoan development, whereas the organization of GRNs that pertain to systems physiology remains mostly unexplored.
My work has focused on delineating the first gene regulatory network of the nematode Caenorhabditis elegans metabolic genes, and investigating how this network relates to the energy homeostasis of the nematode. The resulting metabolic GRN consists of ~70 metabolic genes, 100 TFs and more than 500 protein–DNA interactions. It also includes novel protein-protein interactions involving the metabolic transcriptional cofactor MDT-15 and several TFs that occur in the metabolic GRN. On a global level, we found that the metabolic GRN is enriched for nuclear hormone receptors (NHRs). NHRs form a special class of TFs that can interact with diffusible biomolecules and are well-known regulators of lipid metabolism in other organisms, including humans. Interestingly, NHRs comprise the largest family of TFs in nematodes; the C. elegans genome encodes 284 NHRs, most of which are uncharacterized. In our study, we show that the C. elegans NHRs that we retrieved in the metabolic GRN organize into network modules, and that most of these NHRs function to maintain lipid homeostasis in the nematode. Network modularity has been proposed to facilitate rapid and robust changes in gene expression. Our results suggest that the C. elegans metabolic GRN may have evolved by combining NHR family expansion with the specific modular wiring of NHRs to enable the rapid adaptation of the animal to different environmental cues.
22
Gandhi, Arpita S. "Analysis of time course microarray data for dynamic inference of gene regulatory networks". Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 53 p, 2008. http://proquest.umi.com/pqdweb?did=1605156441&sid=5&Fmt=2&clientId=8331&RQT=309&VName=PQD.
Testo completo
23
Nguyen, Lan K. "Dynamical modelling of feedback gene regulatory networks". Diss., Lincoln University, 2009. http://hdl.handle.net/10182/1340.
Testo completoAbstract (sommario):
Living cells are made up of networks of interacting genes, proteins and other bio-molecules. Simple interactions between network components in forms of feedback regulations can lead to complex collective dynamics. A key task in cell biology is to gain a thorough understanding of the dynamics of intracellular systems and processes. In this thesis, a combined approach of mathematical modelling, computational simulation and analytical techniques, has been used to obtain a deeper insight into the dynamical aspects of a variety of feedback systems commonly encountered in cells. These systems range from model system with detailed available molecular knowledge to general regulatory motifs with varying network structures. Deterministic as well as stochastic modelling techniques have been employed, depending primarily on the specific questions asked. The first part of the thesis focuses on dissecting the principles behind the regulatory design of the Tryptophan Operon system in Escherichia coli. It has evolved three negative feedback loops, namely repression, attenuation and enzyme inhibition, as core regulator mechanisms to control the intracellular level of tryptophan amino acid, which is taken up for protein synthesis. Despite extensive experimental knowledge, the roles of these seemingly redundant loops remain unclear from a dynamical point of view. We aim to understand why three loops, rather than one, have evolved. Using a large-scale perturbation/response analysis through modelling and simulations and novel metrics for transient dynamics quantification, it has been revealed that the multiple negative feedback loops employed by the tryptophan operon are not redundant. In fact, they have evolved to concertedly give rise to a much more efficient, adaptive and stable system, than any single mechanism would provide. Since even the full topology of feedback interactions within a network is insufficient to determine its behavioural dynamics, other factors underlying feedback loops must be characterised to better predict system dynamics. In the second part of the thesis, we aim to derive these factors and explore how they shape system dynamics. We develop an analytical approach for stability and bifurcation analysis and apply it to class of feedback systems commonly encountered in cells. Our analysis showed that the strength and the Hill coefficient of a feedback loop play key role in determining the dynamics of the system carrying the loop. Not only that, the position of the loop was also found to be crucial in this decision. The analytical method we developed also facilitates parameter sensitivity analysis in which we investigate how the production and degradation rates affect system dynamics. We find that these rates are quite different in the way they shape up system behaviour, with the degradation rates exhibiting a more intricate manner. We demonstrated that coupled-loop systems display greater complexity and a richer repertoire of behaviours in comparison with single-loop ones. Different combinations of the feedback strengths of individual loops give rise to different dynamical regimes. The final part of the thesis aims to understand the effects of molecular noise on dynamics of specific systems, in this case the Tryptophan Operon. We developed two stochastic models for the system and compared their predictions to those given by the deterministic model. By means of simulations, we have shown that noise can induce oscillatory behaviour. On the other hand, incorporating noise in an oscillatory system can alter the characteristics of oscillation by shifting the bifurcation point of certain parameters by a substantial amount. Measurement of fluctuations reveals that that noise at the transcript level is most significant while noise at the enzyme level is smallest. This study highlights that noise should not be neglected if we want to obtain a complete understanding of the dynamic behaviour of cells.
24
Karlsson, Fredrik. "Dynamics in Boolean Networks". Thesis, Linköping University, Department of Science and Technology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2888.
Testo completoAbstract (sommario):
In this thesis several random Boolean networks are simulated. Both completely computer generated network and models for biological networks are simulated. Several different tools are used to gain knowledge about the robustness. These tools are Derrida plots, noise analysis and mean probability for canalizing rules. Some simulations on how entropy works as an indicator on if a network is robust are also included. The noise analysis works by measuring the hamming distance between the state of the network when noise is applied and when no noise is applied. For many of the simulated networks two types of rules are applied: nested canalizing and flat distributed rules. The computer generated networks consists of two types of networks: scale-free and ER-networks. One of the conclusions in this report is that nested canalizing rules are often more robust than flat distributed rules. Another conclusion is that the mean probability for canalizing rules has, for flat distributed rules, a very dominating effect on if the network is robust or not. Yet another conclusion is that the probability distribution for indegrees, for flat distributed rules, has a strong effect on if a network is robust due to the connection between the probability distribution for indegrees and the mean probability for canalizing rules.
25
Stefan, Diana. "Structural and parametric identification of bacterial regulatory networks". Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM019/document.
Testo completoAbstract (sommario):
Les technologies expérimentales à haut débit produisent de grandes quantités de données sur les niveaux d'expression des gènes dans les bactéries à l'état d'équilibre ou lors des transitions de croissance.Un défi important dans l'interprétation biologique de ces données consiste à en déduire la topologie du réseau de régulation ainsi que les fonctions de régulation quantitatives des gènes.Un grand nombre de méthodes d'inférence a été proposé dans la littérature. Ces méthodes ont été utilisées avec succès dans une variété d'applications, bien que plusieurs problèmes persistent.Nous nous intéressons ici à l'amélioration de deux aspects des méthodes d'inférence.Premièrement, les données transcriptomiques reflètent l'abondance de l'ARNm, tandis que, le plus souvent, les composants régulateurs sont les protéines codées par les ARNm.Bien que les concentrations de l'ARNm et de protéines soient raisonnablement corrélées à l'état stationnaire, cette corrélation devient beaucoup moins évidente dans les données temporelles acquises lors des transitions de croissance à cause des demi-vies très différentes des protéines et des ARNm.Deuxièmement, la dynamique de l'expression génique n'est pas uniquement contrôlée par des facteurs de transcription et d'autres régulateurs spécifiques, mais aussi par des effets physiologiques globaux qui modifient l'activité de tous les gènes. Par exemple, les concentrations de l'ARN polymérase (libre) et les concentrations des ribosomes (libres) varient fortement avec le taux de croissance. Nous devons donc tenir compte de ces effets lors de la reconstruction d'un réseau de régulation à partir de données d'expression génique.Nous proposons ici une approche expérimentale et computationnelle combinée pour répondre à ces deux problèmes fondamentaux dans l'inférence de modèles quantitatifs de promoteurs bactériens à partir des données temporelles d'expression génique.Nous nous intéressons au cas où la dynamique de l'expression génique est mesurée in vivo et en temps réel par l'intermédiaire de gènes rapporteurs fluorescents. Notre approche d'inférence de réseaux de régulation tient compte des différences de demi-vie entre l'ARNm et les protéines et prend en compte les effets physiologiques globaux.Lorsque les demi-vies des protéines sont connues, les modèles expérimentaux utilisés pour dériver les activités des gènes à partir de données de fluorescence sont intégrés pour estimer les concentrations des protéines.L'état physiologique global de la cellule est estimé à partir de l'activité d'un promoteur de phage, dont l'expression n'est contrôlée par aucun des facteurs de transcription et ne dépend que de l'activité de la machinerie d'expression génique.Nous appliquons l'approche à un module central dans le réseau de régulation contrôlant la motilité et le système de chimiotactisme chez Escherichia coli.Ce module est composé des gènes FliA, FlgM et tar.FliA est un facteur sigma qui dirige l'ARN polymérase vers les opérons codant pour des composants de l'assemblage des flagelles.Le troisième composant du réseau, tar, code pour la protéine récepteur chimiotactique de l'aspartate, Tar, et est directement transcrit par FliA associé à l' holoenzyme ARN polymérase. Le module FliA-FlgM est particulièrement bien adapté pour l'étude des problèmes d'inférence considérés ici, puisque le réseau a été bien étudié et les démivies des protéines jouent un rôle important dans son fonctionnement.Nos résultats montrent que, pour la reconstruction fiable de réseaux de régulation transcriptionelle chez les bactéries, il est nécessaire d'inclure les effets globaux dans le modèle de réseau et d'en déduire de manière explicite les concentrations des protéines à partir des profils d'expression observés, car la demi-vie de l'ARNm et des protéines sont très différentes. Notre approche reste généralement applicable à une grande variété de problèmes d'inférence de réseaux et nous discutons les limites et les extensions possibles de la méthodeHigh-throughput technologies yield large amounts of data about the steady-state levels and the dynamical changes of gene expression in bacteria. An important challenge for the biological interpretation of these data consists in deducing the topology of the underlying regulatory network as well as quantitative gene regulation functions from such data. A large number of inference methods have been proposed in the literature and have been successful in a variety of applications, although several problems remain. We focus here on improving two aspects of the inference methods. First, transcriptome data reflect the abundance of mRNA, whereas the components that regulate are most often the proteins coded by the mRNAs. Although the concentrations of mRNA and protein correlate reasonably during steady-state growth, this correlation becomes much more tenuous in time-series data acquired during growth transitions in bacteria because of the very different half-lives of proteins and mRNA. Second, the dynamics of gene expression is not only controlled by transcription factors and other specific regulators, but also by global physiological effects that modify the activity of all genes. For example, the concentrations of (free) RNA polymerase and the concentration of ribosomes vary strongly with growth rate. We therefore have to take into account such effects when trying to reconstruct a regulatory network from gene expression data. We propose here a combined experimental and computational approach to address these two fundamental problems in the inference of quantitative models of the activity of bacterial promoters from time-series gene expression data. We focus on the case where the dynamics of gene expression is measured in vivo and in real time by means of fluorescent reporter genes. Our network reconstruction approach accounts for the differences between mRNA and protein half-lives and takes into account global physiological effects. When the half-lives of the proteins are available, the measurement models used for deriving the activities of genes from fluorescence data are integrated to yield estimates of protein concentrations. The global physiological state of the cell is estimated from the activity of a phage promoter, whose expression is not controlled by any transcription factor and depends only on the activity of the transcriptional and translational machinery. We apply the approach to a central module in the regulatory network controlling motility and the chemotaxis system in Escherichia coli. This module comprises the FliA, FlgM and tar genes. FliA is a sigma factor that directs RNA polymerase to operons coding for components of the flagellar assembly. The effect of FliA is counteracted by the antisigma factor FlgM, itself transcribed by FliA. The third component of the network, tar, codes for the aspartate chemoreceptor protein Tar and is directly transcribed by the FliA-containing RNA polymerase holoenzyme. The FliA-FlgM module is particularly well-suited for studying the inference problems considered here, since the network has been well-studied and protein half-lives play an important role in its functioning. We stimulated the FliA-FlgM module in a variety of wild-type and mutant strains and different growth media. The measured transcriptional response of the genes was used to systematically test the information required for the reliable inference of the regulatory interactions and quantitative predictive models of gene regulation. Our results show that for the reliable reconstruction of transcriptional regulatory networks in bacteria it is necessary to include global effects into the network model and explicitly deduce protein concentrations from the observed expression profiles. Our approach should be generally applicable to a large variety of network inference problems and we discuss limitations and possible extensions of the method
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Kohutyuk, Oksana. "Retina Workbench a flexible database system for manipulating and mining expression data and genetic regulatory networks /". [Ames, Iowa : Iowa State University], 2007.
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27
Xie, Zhi. "Modelling genetic regulatory networks: a new model for circadian rhythms in Drosophila and investigation of genetic noise in a viral infection process". Phd thesis, Lincoln University. Agriculture and Life Sciences Division, 2007. http://theses.lincoln.ac.nz/public/adt-NZLIU20070712.144258/.
Testo completoAbstract (sommario):
In spite of remarkable progress in molecular biology, our understanding of the dynamics and functions of intra- and inter-cellular biological networks has been hampered by their complexity. Kinetics modelling, an important type of mathematical modelling, provides a rigorous and reliable way to reveal the complexity of biological networks. In this thesis, two genetic regulatory networks have been investigated via kinetic models.
In the first part of the study, a model is developed to represent the transcriptional regulatory network essential for the circadian rhythms in Drosophila. The model incorporates the transcriptional feedback loops revealed so far in the network of the circadian clock (PER/TIM and VRI/PDP1 loops). Conventional Hill functions are not used to describe the regulation of genes, instead the explicit reactions of binding and unbinding processes of transcription factors to promoters are modelled. The model is described by a set of ordinary differential equations and the parameters are estimated from the in vitro experimental data of the clocks components. The simulation results show that the model reproduces sustained circadian oscillations in mRNA and protein concentrations that are in agreement with experimental observations. It also simulates the entrainment by light-dark cycles, the disappearance of the rhythmicity in constant light and the shape of phase response curves resembling that of experimental results. The model is robust over a wide range of parameter variations. In addition, the simulated E-box mutation, perS and perL mutants are similar to that observed in the experiments. The deficiency between the simulated mRNA levels and experimental observations in per01, tim01 and clkJrk mutants suggests some differences in the model from reality. Finally, a possible function of VRI/PDP1 loops is proposed to increase the robustness of the clock.
In the second part of the study, the sources of intrinsic noise and the influence of extrinsic noise are investigated on an intracellular viral infection system. The contribution of the intrinsic noise from each reaction is measured by means of a special form of stochastic differential equation, the chemical Langevin equation. The intrinsic noise of the system is the linear sum of the noise in each of the reactions. The intrinsic noise arises mainly from the degradation of mRNA and the transcription processes. Then, the effects of extrinsic noise are studied by means of a general form of stochastic differential equation. It is found that the noise of the viral components grows logarithmically with increasing noise intensities. The system is most susceptible to noise in the virus assembly process. A high level of noise in this process can even inhibit the replication of the viruses.
In summary, the success of this thesis demonstrates the usefulness of models for interpreting experimental data, developing hypotheses, as well as for understanding the design principles of genetic regulatory networks.
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Al-Musawi, Ahmad Jr. "COMPLEX NETWORK GROWING MODEL USING DOWNLINK MOTIFS". VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3088.
Testo completoAbstract (sommario):
Understanding the underlying architecture of gene regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics as it can provide insights in disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs, which are small subgraphs of specific types and appear more abundantly in GRNs than in other randomized networks. In fact, such motifs are considered to be the building blocks of GRNs (and other complex networks) and they help achieve the underlying robustness demonstrated by most biological networks. The goal of this thesis is to design biological network (specifically, GRN) growing models. As the motif distribution in networks grown using preferential attachment based algorithms do not match that of the GRNs seen in model organisms like E. coli and yeast, we hypothesize that such models at a single node level may not properly reproduce the observed degree and motif distributions of biological networks. Hence, we propose a new network growing algorithm wherein the central idea is to grow the network one motif (specifically, we consider one downlink motif) at a time. The accuracy of our proposed algorithm was evaluated extensively and show much better performance than existing network growing models both in terms of degree and motif distributions. We also propose a complex network growing game that can identify important strategies behind motif interactions by exploiting human (i.e., gamer) intelligence. Our proposed gaming software can also help in educational purposes specifically designed for complex network studies.
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Nakajima, Natsu. "Genetic Network Completion Using Dynamic Programming and Least-Squares Fitting". 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/195987.
Testo completo
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Xiong, Hao. "Constrained expectation-maximization (EM), dynamic analysis, linear quadratic tracking, and nonlinear constrained expectation-maximation (EM) for the analysis of genetic regulatory networks and signal transduction networks". Thesis, [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2332.
Testo completo
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Sikkink, Kristin. "Experimental Evolution of Phenotypic Plasticity for Stress Resistance in the Nematode Caenorhabditis remanei". Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18425.
Testo completoAbstract (sommario):
Many organisms can acclimate to new environments through phenotypic plasticity, a complex trait that can be heritable, be subject to selection, and evolve. However, the rate and genetic basis of plasticity evolution remain largely unknown. Experimentally evolved populations of the nematode Caenorhabditis remanei were created by selecting for stress resistance under different environmental conditions. This resource was used to address key questions about how phenotypic plasticity evolves and what the genetic basis of plasticity is. Here, I highlight ways in which a fuller understanding of the environmental context influences our interpretation of the evolution of phenotypic plasticity. In a population selected to withstand heat stress, an apparent case of genetic assimilation did not show correlated changes in global gene regulation. However, further investigation revealed that the induced plasticity was not fixed across environments, but rather the threshold for the response was shifted over evolutionary time. Similarly, the past environment experienced by populations can play a role in directing the multivariate response to selection. Correlated responses to selection between traits and across environments were examined. The pattern of covariation in the evolutionary response among traits differed depending on the environment in which selection occurred, indicating that there exists variation in pleiotropy across the stress response network that is highly sensitive to the external environment. To understand how the patterns of pleiotropy are altered by environment and evolution, there is a pressing need to determine the structure of the molecular networks underlying plastic phenotypes. Using RNA-sequencing, the structure of the gene regulatory network is examined for a subset of evolved populations from one environment. Key modules within this network were identified that are strong candidates for the evolution of phenotypic plasticity in this system. Together, the data presented in this dissertation provide a comprehensive view of the myriad ways in which the environment shapes the genetic architecture of stress response phenotypes and directs the evolution of phenotypic plasticity. Additionally, the structure of transcriptional network provides valuable insight into the genetic basis of adaptation to environmental change and the evolution of phenotypic plasticity.
This dissertation includes both previously published and co-authored material.
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Marchand, Gwenaëlle. "Gene regulatory networks involved in drought stress responses : identification, genetic control and variability in cultivated sunflower, Helianthus annuus and its relatives". Toulouse 3, 2014. http://thesesups.ups-tlse.fr/2597/.
Testo completoAbstract (sommario):
La sécheresse affecte le rendement des plantes de grande culture comme le tournesol. Ces plantes développent des réponses morpho-physiologiques pour améliorer leur tolérance au manque d'eau. De nombreux gènes formant un réseau de régulation (GRN) contribuent à un contrôle génétique complexe de ces réponses. Le travail présenté étudie ce réseau, ses différents gènes et leurs interactions chez le tournesol. Tout d'abord, nous avons mis en évidence trois gènes récepteurs du signal environnemental afin de construire un biomarqueur du statut hydrique. Puis, par une étude d'association, nous avons reconstruit le GRN reliant les gènes de réponse au stress et déchiffré leur contrôle génétique. Enfin, par une approche de biologie des systèmes, nous avons inféré le GRN groupant des gènes de régulation et de réponse. Cette étude nous a permis d'identifier des mécanismes majeurs de tolérance à la sécheresse chez le tournesol, ainsi que le rôle de ce réseau dans l'évolution du genre HeliantusDrought is a major stress that affects growth, physiology and therefore yield of crops as sunflower. To become more tolerant, plants develop complex morpho-physiological responses. Various genes interacting between them and with the environment are involved in the genetic control of those responses. They form together a gene regulatory network (GRN). Here, we focused on these drought GRN, its different gene groups and their interactions in the cultivated sunflower. First, we highlighted three genes reflecting the environmental signal. From their expression we built a plant water status biomarker. Then through an association study, we built the GRN connecting drought responsive genes and we deciphered their genetic control. Finally, thanks to a systems biology approach we inferred the GRN linking regulatory and drought responsive genes. Studying this network, we examined how it could drive phenotypic changes and how it was related to Heliantus evolution and sunflower breeding
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Abdelmotaleb, Ahmed Mostafa Othman. "Evolution of spiking neural networks for temporal pattern recognition and animat control". Thesis, University of Hertfordshire, 2016. http://hdl.handle.net/2299/17181.
Testo completoAbstract (sommario):
I extended an artificial life platform called GReaNs (the name stands for Gene Regulatory evolving artificial Networks) to explore the evolutionary abilities of biologically inspired Spiking Neural Network (SNN) model. The encoding of SNNs in GReaNs was inspired by the encoding of gene regulatory networks. As proof-of-principle, I used GReaNs to evolve SNNs to obtain a network with an output neuron which generates a predefined spike train in response to a specific input. Temporal pattern recognition was one of the main tasks during my studies. It is widely believed that nervous systems of biological organisms use temporal patterns of inputs to encode information. The learning technique used for temporal pattern recognition is not clear yet. I studied the ability to evolve spiking networks with different numbers of interneurons in the absence and the presence of noise to recognize predefined temporal patterns of inputs. Results showed, that in the presence of noise, it was possible to evolve successful networks. However, the networks with only one interneuron were not robust to noise. The foraging behaviour of many small animals depends mainly on their olfactory system. I explored whether it was possible to evolve SNNs able to control an agent to find food particles on 2-dimensional maps. Using ring rate encoding to encode the sensory information in the olfactory input neurons, I managed to obtain SNNs able to control an agent that could detect the position of the food particles and move toward it. Furthermore, I did unsuccessful attempts to use GReaNs to evolve an SNN able to control an agent able to collect sound sources from one type out of several sound types. Each sound type is represented as a pattern of different frequencies. In order to use the computational power of neuromorphic hardware, I integrated GReaNs with the SpiNNaker hardware system. Only the simulation part was carried out using SpiNNaker, but the rest steps of the genetic algorithm were done with GReaNs.
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Crocetti, Guilherme Martins. "Halobacterium salinarum NRC-1: rede de regulação gênica e sua análise probabilística". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-31052018-172109/.
Testo completoAbstract (sommario):
Este trabalho teve como objetivo principal modelar a Rede de Regulação Gênica do organismo modelo Halobacterium salinarum NRC-1, estabelecendo interações entre as entidades da rede por intermédio de experimentos inéditos de interação física: ChIP- *, RIP-* e dRNA-seq. Em contraponto com as abordagens clássicas de construção de redes, que estimam interações através de medições de expressão gênica, este trabalho as estabeleceu exclusivamente de interações físicas, permitindo que a estrutura final seja uma representação mais fiel ao fenômeno físico de regulação gênica, baseando-se nos fundamentos da Biologia Sistêmica. Em vista da abundância de dados públicos de expressão gênica para o organismo e do objetivo primário, um objetivo secundário foi traçado: identificar, computacionalmente, genes de fato controlados pelas interações fornecidas pela nova rede. Para isso, a estrutura estabelecida foi transformada numa Rede Bayesiana, e a identificação de genes foi efetuada através da análise de suas Tabelas de Probabilidade Condicionais. Finalmente, como os resultados obtidos para o objetivo secundário foram desfavoráveis a utilização de Redes Bayesianas, os resultados efetivos deste trabalho foram a criação de uma nova Rede de Regulação Gênica para a H. salinarum e uma análise em torno da efetividade de Redes Bayesianas neste contexto.The main goal of this work was modeling the gene regulatory network of the model organism Halobacterium salinarum NRC-1, establishing new interactions between networks entities through unpublished physical interaction experiments: ChIP-*, RIP-* e dRNA-seq. Instead of using classical approaches to build network structures that estimates interactions using gene expression data, this work established them exclusively from physical interactions. Therefore, the final structure is a more reliable representation of the physical phenomenon of gene expression, built using the principles of systems biology. Considering the amount of public available gene expression data and the primary goal, another objective was proposed: a computational analysis to detect genes actually controlled by the interactions of the new network. To achieve this goal the established network was transformed in a Bayesian network, detecting genes through the analysis of their conditional probability tables. Lastly, as the results of the secondary goal went against the use of Bayesian networks, the effective results of this thesis were the creation of a new genetic regulatory network for H. salinarum and an analysis around Bayesian networks in this context.
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Buck, Moritz. "Towards the evolution of multicellularity : a computational artificial life approach". Thesis, University of Hertfordshire, 2011. http://hdl.handle.net/2299/6409.
Testo completoAbstract (sommario):
Technology, nowadays, has given us huge computational potential, but computer sciences have major problems tapping into this pool of resources. One of the main issues is how to program and design distributed systems. Biology has solved this issue about half a billion years ago, during the Cambrian explosion: the evolution of multicellularity. The evolution of multicellularity allowed cells to differentiate and so divide different tasks to different groups of cells; this combined with evolution gives us a very good example of how massively parallel distributed computational system can function and be “programmed”. However, the evolution of multicellularity is not very well understood, and most traditional methodologies used in evolutionary theory are not apt to address and model the whole transition to multicellularity. In this thesis I develop and argue for new computational artificial life methodologies for the study of the evolution of multicellularity that are able to address the whole transition, give new insights, and complement existing methods. I argue that these methodologies should have three main characteristics: accessible across scientific disciplines, have potentiality for complex behaviour, and be easy to analyse. To design models, which possess those characteristics, I developed a model of genetic regulatory networks (GRNs) that control artificial cells, which I have used in multiple evolutionary experiments. The first experiment was designed to present some of the engineering problems of evolving multicelled systems (applied to graph-colouring), and to perfect my artificial cell model. The two subsequent experiments demonstrate the characteristics listed above: one model based on a genetic algorithm with an explicit two-level fitness function to evolve multicelled cooperative patterning, and one with freely evolving artificial cells that have evolved some multicelled cooperation as evidenced by novel measures, and has the potential to evolve multicellularity. These experiments show how artificial life models of evolution can discover and investigate new hypotheses and behaviours that traditional methods cannot.
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Watson, Emma. "Diet-responsive Gene Networks Rewire Metabolism in the Nematode Caenorhabditis elegans to Provide Robustness against Vitamin B12 Deficiency: A Dissertation". eScholarship@UMMS, 2015. https://escholarship.umassmed.edu/gsbs_diss/801.
Testo completoAbstract (sommario):
Maintaining cellular homeostasis is a complex task, which involves monitoring energy states and essential nutrients, regulating metabolic fluxes to accommodate energy and biomass needs, and preventing buildup of potentially toxic metabolic intermediates and byproducts. Measures aimed at maintaining a healthy cellular economy inherently depend on the composition of nutrients available to the organism through its diet. We sought to delineate links between dietary composition, metabolic gene regulation, and physiological responses in the model organism C. elegans.
As a soil-dwelling bacterivore, C. elegans encounters diverse bacterial diets. Compared to a diet of E. coli OP50, a diet of Comamonas aquatica accelerates C. elegans developmental rate, alters egg-laying dynamics and shortens lifespan. These physiological responses are accompanied by gene expression changes. Taking advantage of this natural, genetically tractable predator-prey system, we performed genetic screens i) in C. elegans to identify regulators of diet-responsive genes, and ii) in E. coli and Comamonas to determine dietary factors driving transcriptional responses in C. elegans. We identified a C. elegans transcriptional program that regulates metabolic genes in response to vitamin B12 content in the bacterial diet. Interestingly, several B12- repressed metabolic genes of unknown function are highly activated when B12- dependent propionyl-CoA breakdown is impaired, and inactivation of these genes renders animals sensitive to propionate-induced toxicity. We provide genetic and metabolomic evidence in support of the hypothesis that these genes form a parallel, B12-independent, β-oxidation-like propionate breakdown shunt in C. elegans, similar to the pathway utilized by organisms like yeast and plants that do not use vitamin B12.
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Watson, Emma. "Diet-responsive Gene Networks Rewire Metabolism in the Nematode Caenorhabditis elegans to Provide Robustness against Vitamin B12 Deficiency: A Dissertation". eScholarship@UMMS, 2009. http://escholarship.umassmed.edu/gsbs_diss/801.
Testo completoAbstract (sommario):
Maintaining cellular homeostasis is a complex task, which involves monitoring energy states and essential nutrients, regulating metabolic fluxes to accommodate energy and biomass needs, and preventing buildup of potentially toxic metabolic intermediates and byproducts. Measures aimed at maintaining a healthy cellular economy inherently depend on the composition of nutrients available to the organism through its diet. We sought to delineate links between dietary composition, metabolic gene regulation, and physiological responses in the model organism C. elegans.
As a soil-dwelling bacterivore, C. elegans encounters diverse bacterial diets. Compared to a diet of E. coli OP50, a diet of Comamonas aquatica accelerates C. elegans developmental rate, alters egg-laying dynamics and shortens lifespan. These physiological responses are accompanied by gene expression changes. Taking advantage of this natural, genetically tractable predator-prey system, we performed genetic screens i) in C. elegans to identify regulators of diet-responsive genes, and ii) in E. coli and Comamonas to determine dietary factors driving transcriptional responses in C. elegans. We identified a C. elegans transcriptional program that regulates metabolic genes in response to vitamin B12 content in the bacterial diet. Interestingly, several B12- repressed metabolic genes of unknown function are highly activated when B12- dependent propionyl-CoA breakdown is impaired, and inactivation of these genes renders animals sensitive to propionate-induced toxicity. We provide genetic and metabolomic evidence in support of the hypothesis that these genes form a parallel, B12-independent, β-oxidation-like propionate breakdown shunt in C. elegans, similar to the pathway utilized by organisms like yeast and plants that do not use vitamin B12.
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Chen, Ye. "Fuzzy Cognitive Maps: Learning Algorithms and Biomedical Applications". University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1423581705.
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Scofield, Michael D. "Elucidating the Transcriptional Network Underlying Expression of a Neuronal Nicotinic Receptor Gene: A Dissertation". eScholarship@UMMS, 2010. https://escholarship.umassmed.edu/gsbs_diss/497.
Testo completoAbstract (sommario):
Neuronal nicotinic acetylcholine receptors (nAChRs) are involved in a plethora of fundamental biological processes ranging from muscle contraction to the formation of memories. The studies described in this work focus on the transcriptional regulation of the CHRNB4 gene, which encodes the ß4 subunit of neuronal nAChRs. We previously identified a regulatory sequence (5´– CCACCCCT –3´), or “CA box”, critical for CHRNB4 promoter activity in vitro. Here I report transcription factor interaction at the CA box along with an in vivo analysis of CA box transcriptional activity. My data indicate that Sp1, Sp3, Sox10 and c-Jun interact with the CHRNB4 CA box in the context of native chromatin. Using an in vivo transgenic approach in mice, I demonstrated that a 2.3-kb fragment of the CHRNB4 promoter region, containing the CA box, is capable of directing cell-type specific expression of a reporter gene to many of the brain regions that endogenously express the CHRNB4 gene. Site-directed mutagenesis was used to test the hypothesis that the CA box is critical for CHRNB4 promoter activity in vivo. Transgenic animals were generated in which LacZ expression is driven by a mutant form of the CA box. Reporter gene expression was not detected in any tissue or cell type at ED18.5. Similarly, I observed dramatically reduced reporter gene expression at PD30 when compared to wild type transgenic animals, indicating that the CA box is an important regulatory feature of the CHRNB4 promoter. ChIP analysis of brain tissue from mutant transgenic animals demonstrated that CA box mutation results in decreased interaction of the transcription factor Sp1 with the CHRNB4 promoter. I have also investigated transcription factor interaction at the CHRNB4 promoter CT box, (5´– ACCCTCCCCTCCCCTGTAA –3´) and demonstrated that hnRNP K interacts with the CHRNB4 promoter in an olfactory bulb derived cell line. Surprisingly, siRNA experiments demonstrated that hnRNP K knockdown has no impact on CHRNA5, CHRNA3 or CHRNB4 gene expression. Interestingly, knockdown of the transcription factor Purα results in significant decreases in CHRNA5, CHRNA3 and CHRNB4 mRNA levels. These data indicate that Purα can act to enhance expression of the clustered CHRNA5, CHRNA3 and CHRNB4 genes. Together, these results contribute to a more thorough understanding of the transcriptional regulatory mechanisms underlying expression of the CHRNB4 as well as the CHRNA5 and CHRNA3 genes, critical components of cholinergic signal transduction pathways in the nervous system.
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Odorico, Andréas. "Modéliser l'évolution de la relation génotype-phénotypes dans des réseaux de régulation". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS537/document.
Testo completoAbstract (sommario):
L’identification de l’information génétique comme support de l’hérédité a accordé aux gènes une importance majeure dans l’étude de l’évolution et des mécanismes permettant la mise en place des caractères. Cependant, les processus permettant à une variation génétique de se traduire en variation phénotypique sont complexes et leur identification est centrale pour la compréhension de l’évolution.On parle de relation génotype-phénotype pour désigner la fonction qui relie l’espace des gènes à celui des caractères. Étudier les propriétés de cette relation permet d’identifier des mécanismes pouvant altérer les trajectoires évolutives et améliorer notre compréhension de l’évolution de systèmes vivants. Je défends notamment l’intérêt d’étudier mécanistiquement les processus par lesquels une variation génétique donne naissance à une variation phénotypique, et emploie, pour ce faire, un modèle de réseau de régulation transcriptionnelle.Ici, j’étudie les effets d’une information environnementale sur la relation génotype-phénotype et ses propriétés (notamment sa canalisation, sa robustesse à des perturbations génétiques ou environnementales). Pour ce faire, l’évolution de réseaux de régulation simulés est étudiée en présence d’un gène senseur de l’environnement ou d’une forme d’hérédité non génétique.Ce manuscrit débute par une discussion générale de l’intérêt des approches par modélisation, notamment pour l’étude de phénomènes complexes. Enfin, les résultats obtenus sont présentés en regard des discussions sur la nécessité d’une « synthèse évolutive étendue » pour décrire le processus évolutif d’une manière difficilement accessible par une approche gène-centréeThe identification of genetic information as the as a physical basis for heredity put genes in the spotlight for the study of evolution and of the mechanisms shaping characters. However, the processes allowing genetic variation to translate into phenotypic variation are complex and their identification is crucial for the study of evolution.Genotype-phenotype relationship designates the function connecting the genotype and the phenotype spaces. Studying its properties will shed the light on mechanisms able to alter evolutionary trajectories and improve our understanding of the evolutionary process. I defend the importance of a mechanistic study of the processes translating genetic variation into a phenotypic one and use a model of transcriptional regulation networks to do so.This study tackles the topic of the effects of an environmental information on the genotype-phenotype relationship and its properties (especially canalization, the robustness of a phenotype to genetic or environmental disturbances). To do so, I studied the evolution of simulated regulatory networks in presence of a gene acting as an environmental sensor as well as in presence of non genetic inheritance.This document begins with a general discussion on the purpose of modelling approaches and the insights they bring on the study of complex phenomena. The results are discussed in the light of the debates on the necessity of an « evolutionary extended synthesis » to describe the evolutionary processes in a way hardly available with the gene-centered approach
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Bezerra, George Barreto Pereira. "Aplicações de computação bioinspirada em bioinformatica : investigando o papel dos genes e suas interações". [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259068.
Testo completoAbstract (sommario):
Orientador: Fernando Jose Von ZubenDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
Made available in DSpace on 2018-08-11T13:03:57Z (GMT). No. of bitstreams: 1 Bezerra_GeorgeBarretoPereira_M.pdf: 1423598 bytes, checksum: 5587c3941203fcdd6c2eddb7dad89a93 (MD5) Previous issue date: 2006
Resumo: Esta dissertação trata das redes gênicas, o mecanismo de controle da ativação dos genes nas células, sob três perspectivas computacionais diferentes. Inicialmente, sob uma ótica de engenharia, é elaborada uma ferramenta de inferência de redes gênicas, capaz de reconstruir a estrutura estática dessas redes a partir de um conjunto de dados experimentais. O método proposto para essa tarefa de identificação de sistemas é especialmente projetado para conjunto de dados reduzidos, um cenário bastante comum quando se trata de dados de expressão gênica. Numa segunda etapa, é proposto um modelo computacional das redes gênicas, em que as reações bioquímicas que ocorrem na célula são vistas como equações não-lineares arranjadas numa estrutura conexionista. Desta vez, ao invés de inferir redes existentes, esse modelo é utilizado em conjunto com uma abordagem evolutiva para sintetizar redes gênicas artificiais capazes de realizar tarefas dinâmicas ¿ em específico, para solucionar um problema clássico de robótica evolutiva. Embora o modelo seja empregado como técnica de resolução de problemas, o objetivo agora é mais no sentido científico, isto é, as redes gênicas artificiais evoluídas são analisadas como modelos que podem ajudar a compreender propriedades observadas nos sistemas naturais. Finalmente, a terceira etapa consiste numa abordagem conceitual. O propósito principal é tentar compor um novo cenário para o estudo das redes gênicas, reunindo conceitos e dados empíricos de outras áreas da ciência moderna, como a neurociência e a sinergética, e investigando as implicações de uma nova ótica para o processamento de informação celular. O objetivo aqui é voltado para a compreensão dos mecanismos de processamento de informação em organismos vivos
Abstract: This dissertation deals with genetic networks, the mechanism of control of gene activity in cells, under three different computational perspectives. Initially, as an engineering approach, a computational tool for inference of genetic networks is proposed, which is able to recover the static structure of these networks from experimental datasets. This systems identification method is especially designed for small datasets, a common scenario when coping with gene expression data. In the second step, a computational model for genetic networks is proposed, in which biochemical reactions that occur inside the cell are treated as nonlinear equations in a connectionist structure. Rather than inferring networks from data, this model is used together with an evolutionary algorithm to synthesize artificial genetic networks that are able to solve dynamic tasks ¿ and in particilar, to solve a classic problem in evolutionary robotics. Although the model is used as a problem-solving technique, the objective here is primarily scientific, i.e., the evolved artificial genetic networks are viewed as an opportunity to study properties observed in natural systems. Finally, the third step comprises a conceptual approach, in which ideas from other fields of modern science, like neuroscience and synergetics, are put together to compose a new scenario to the study of the information processing in genetic networks
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica
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Muhammad, Ridzuan Mohd Ikhwan Bin. "Reliability assessment of distribution networks incorporating regulator requirements, generic network equivalents and smart grid functionalities". Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29009.
Testo completoAbstract (sommario):
Over the past decades, the concepts and methods for reliability assessment have evolved from analysing the ability of individual components to operate without faults and as intended during their lifetime, into the comprehensive approaches for evaluating various engineering strategies for system planning, operation and maintenance studies. The conventional reliability assessment procedures now receive different perspectives in different engineering applications and this thesis aims to improve existing approaches by incorporating in the analysis: a) a more detailed and accurate models of LV and MV networks and their reliability equivalents, which are important for the analysis of transmission and sub-transmission networks, b) the variations in characteristics and parameters of LV and MV networks in different areas, specified as “generic” UK/Scottish highly-urban, urban, sub-urban and rural network models, c) the relevant requirements for network reliability performance imposed by Regulators on network operators, d) the actual aggregate load profiles of supplied customers and their correlation with typical daily variations of fault probabilities and repair times of considered network components, and e) some of the expected “smart grid” functionalities, e.g., increased use of network automation and reconfiguration schemes, as well as the higher penetration levels of distributed generation/storage resources. The conventional reliability assessment procedures typically do not include, or only partially include the abovementioned important factors and aspects in the analysis. In order to demonstrate their importance, the analysis presented in the thesis implements both analytical and probabilistic reliability assessment methods in a number of scenarios and study cases with improved and more detailed “generic” LV and MV network models and their reliability equivalents. Their impact on network reliability performance is analysed and quantified in terms of the frequency and duration of long and short supply interruptions (SAIFI and SAIDI), as well as energy not supplied (ENS). This thesis addresses another important aspect of conventional approaches, which often, if not always, provide separate indicators for the assessment of system-based reliability performance and for the assessment of customer-based reliability performance. The presented analysis attempts to more closely relate system reliability performance indicators, which generally correspond to a fictitious “average customer”, to the actual “best-served” and “worst-served” customers in the considered networks. Here, it is shown that a more complex metric than individual reliability indicators should be used for the analysis, as there are different best-served and worst-served customers in terms of the frequency and duration of supply interruptions, as well as amounts of not supplied energy. Finally, the analysis in the thesis considers some aspects of the anticipated transformation of existing networks into the future smart grids, which effectively require to re-evaluate the ways in which network reliability is approached at both planning and operational stages. Smart grids will feature significantly higher penetration levels of variable renewable-based distributed generation technologies (with or without energy storage), as well as the increased operational flexibility, automation and remote control facilities. In this context, the thesis evaluates some of the considered smart grid capabilities and functionalities, showing that improved system reliability performance might result in a deterioration of power quality performance. This is illustrated through the analysis of applied automation, reconfiguration and automatic reclosing/remote switching schemes, which are shown to reduce frequency and duration of long supply interruptions, but will ultimately result in more frequent and/or longer voltage sags and short interruptions. Similarly, distributed generation/storage resources might have strong positive impact on system reliability performance through the reduced power flows in local networks and provision of alternative supply points, even allowing for a fully independent off-grid operation in microgrids, but this may also result in the reduced power quality levels within the microgrids, or elsewhere in the network, e.g. due to a higher number of switching transfers and transients.
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Boualia, Sami. "The gene regulatory network of early kidney development". Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107584.
Testo completoAbstract (sommario):
Congenital anomalies of the kidney and urinary tract (CAKUT) are the most common cause of chronic kidney disease in infants. CAKUT includes a spectrum of urinary tract anomalies that can be found alone or in combination, such as vesicoureteral reflux, and duplex kidneys. Murine models of these defects have been instrumental in identifying novel genetic regulators of both normal and aberrant kidney development. In mammals, kidney formation proceeds through three phases; pro-, meso- and metanephric development. Gene inactivation studies have identified essential regulators at each stage of normal kidney development. Specifically, the individual roles of transcription factors (TFs) Pax2, Gata3, Lim1, Emx2 and Evi1 in urogenital system development have been described by our lab and others. Here we explore the genetic network of interactions and cooperativity between these TFs in the context of normal and aberrant urogenital system (UGS) development. In order to establish a genetic transcriptional hierarchy between Pax2, Gata3, Lim1, Emx2 and Evi1, we investigated the expression of these TFs, as well as selected downstream developmental effectors, in each homozygous mutant embryo. In addition, we performed microarray analyses on the mesonephros of Pax2 and Gata3 mutant embryos, establishing their genetic expression profiles. Through these complementary approaches, we have established a genetic network in which Pax2, Gata3 and Lim1 are at the top of a transcriptional hierarchy during early kidney development. Having identified Pax2, Gata3 and Lim1 as the 'core' regulators of mesonephric development, we then addressed which interactions were direct via extensive chromatin immunoprecipitation (ChIP-PCR) experiments from cell lines expressing tagged recombinant versions of Pax2, Gata3 and Lim1. We found that both Pax2 and Gata3 associate to Lim1 enhancers. We also identified Gata3 binding sites in enhancers of multiple cellular effectors, including Formin1, Diap1 and Ret. In addition, we found that Lim1 associates with enhancers on the extracellular matrix protein Npnt locus. Finally, we confirmed through in vitro reporter assays that Gata3 activates a Gata-binding site in the Ret promoter region, thus identifying Ret as a direct target gene of Gata3 in the kidney. Finally, we tested the genetic cooperativity between Pax2, Emx2, Gata3, Lim1, and Evi1 by screening a series of compound heterozygous mice for UGS anomalies. We found a high incidence of urinary tract anomalies in Pax2;Emx2 compound heterozygotes that are not found in either single heterozygotes or other allellic combinations. Pax2+/-;Emx2+/- mice harbour duplex systems associated with urinary tract obstruction, bifid ureter and a high penetrance of vesicoutreteral reflux. Remarkably, most compound heterozygotes display lower intravesical reflux pressure compared to controls. Earlier analysis of Pax2+/-;Emx2+/- embryos identified ureter budding defects as the primary cause of urinary tract anomalies. We additionally establish Pax2 as a direct regulator of Emx2 expression in the mesonephric duct. Together, these results identified a haploinsufficient combination of transcriptional kidney regulators resulting in a novel CAKUT-like phenotype in mouse.Les anomalies congénitales du rein et des canaux urinaires (CAKUT) sont les causes les pus communes de maladies rénales chroniques chez les enfants en bas âge. CAKUT comprend un spectre d'anomalies des canaux urinaires qui se manifestent seules ou en combinaison, telles le reflux vesico-urétéral et les reins dupliqués. Les modèles de souris ont été instrumentaux dans l'identification de nouveaux régulateurs du dévelopment rénal. Chez les mammifères, le dévelopement rénal se fait en trois phases, les stades pro-, meso- et métanéphriques. Les études d'inactivations génétiques ont permis d'identifier des régulateurs essentiels à chaque stade du dévelopement rénal. Spécifiquement, les rôles individuels des facteurs de transcription Pax2, Gata3, Lim1, Emx2 et Evi1 lors du dévelopement du système urogénital ont été décrits aussi bien par notre laboratoire que par d'autres. Ici, nous explorons le réseau d'interactions et de coopérativité génétiques entre ces facteurs de transcription dans le contexte du dévelopement normal et aberrant du système urogénital. Afin d'établir la hiérarchie génétique transcriptionelle entre Pax2, Gata3, Lim1, Emx2 et Evi1, nous avons procédé à l'analyse de l'expression de ces facteurs, ainsi qu'à une sélection d'effecteurs dévelopementaux en aval, dans chacun des embryons mutants homozygotes respectifs. De plus, nous avons réalisé les profils d'expression génétique de Pax2 et de Gata3 par des analyses sur micropuces. Avec ces approches complémentaires, nous avons établi un réseau génétique dans lequel Pax2, Gata3 et Lim1 sont au sommet de la hiérarchie transcriptionelle lors du dévelopment rénal. Ayant identifié Pax2, Gata3 et Lim1 comme étant le 'noyau' régulateur du dévelopement mésonéphrique, nous avons ensuite déterminé quelles interactions étaient directes grâce à des immunoprécipitations de chromatine dans des lignées cellulaires exprimant des versions étiquettées (tagged) de Pax2, Gata3 et Lim1. Nous avons déterminé que Pax2 et Gata3 se lient au locus de Lim1. Nous avons également trouvé des sites où Gata3 se lie sur les locus d'effecteurs cellulaires, nottament Formin1, Diap1 ainsi que Ret. De surcroît, nous avons déterminé que Lim1 s'associe au locus de la protéine de la matrice extracellulaire Npnt. Nous avons également confirmé, grâce à une analyse in vitro, que Gata3 active un site dans la région du promoteur de Ret, identifiant ainsi Ret comme un gène cible direct de Gata3 dans le rein. Finalement, nous avons testé la coopérativité génétique entre Pax2, Gata3 Lim1, Emx2 et Evi1 en dépistant des anomalies urogénitales dans une série d'embryons de souris doubles hétérozygotes. Nous avons trouvé une haute incidence d'anomalies des canaux urinaires chez les embryons doubles hétérozygotes Pax2;Emx2, qui ne sont présentes ni chez les homozygotes ni dans les autres combinaisons alléliques. Les souris Pax2+/-;Emx2+/- ont des systèmes dupliqués associés avec des obstructions de l'uretère, des uretères bifides et une haute pénétrance de reflux vésico-urétéral. En effet, la plupart des doubles hétérozygotes ont une pression intravésicale de reflux moindre que les contrôles. L'analyse précoce des embryons Pax2+/-;Emx2+/- a permis d'identifier des défauts de bourgeonnement urétéral comme cause primaire des anomalies des canaux urinaires. De sucroît, nous avons établi Pax2 comme un régulateur direct de l'expression de Emx2 dans le tube mésonéphrique. Ensemble, ces résultats ont permis d'identifier une combinaison génétique entre deux facteurs de transcription résultant en un nouveau phénotype ressemblant au CAKUT chez la souris.
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Jiménez, Ray Dueñas. "Algoritmos genéticos em inferência de redes gênicas". reponame:Repositório Institucional da UFABC, 2014.
Cerca il testo completo
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Irons, David James. "Novel applications of Boolean network models to genetic regulatory systems". Thesis, University of Sheffield, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434502.
Testo completo
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Deyell, Matthew. "Multiplexed Genetic Perturbations of the Regulatory Network of E. coli". Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC175/document.
Testo completoAbstract (sommario):
Malgré les progrès réalisés dans le séquençage de l’ADN, nous n’avons pas encore compris comment le phénotype d’un organisme se rapporte au contenu de son génome. Cependant, il est devenu clair que l'impact des gènes dépend du contexte. La simple présence d'un gène dans un génome ne nous informe pas du moment où il est exprimé et des autres gènes qui y sont exprimés. Comprendre comment l'expression des gènes est régulée est un élément nécessaire pour comprendre comment les phénotypes émergent d'un génotype donné. Les facteurs de transcription, qui peuvent activer ou réprimer l'expression d'un gène, forment un réseau complexe d'interactions entre eux et leurs gènes ciblés. Ce réseau consiste en une hiérarchie de groupes de facteurs de transcription fortement liés, chacun lié à des processus cellulaires distincts. La structure de ce réseau de régulation transcriptionnelle est-elle significative pour la réponse transcriptionnelle d'une cellule? Ici, nous utilisons une protéine de liaison à l'ADN programmable appelée CRISPR (répétitions courtes palindromiques groupées régulièrement) pour perturber l'expression génique des régulateurs globaux au sein du réseau de régulation transcriptionnelle. Ces régulateurs mondiaux régulent de nombreux processus cellulaires distincts et ont de nombreuses cibles génétiques. Le système CRISPR nous permet de perturber ces régulateurs dans toutes les combinaisons possibles, y compris les perturbations d'ordre supérieur avec tous les régulateurs mondiaux potentiellement ciblés perturbés en même temps. Nous enregistrons ensuite à la fois le modèle d'expression du transciptome en utilisant le séquençage de l'ARN et l'adéquation de chaque souche. Nous trouvons que la structure du réseau de régulation augmente la dimensionnalité de la réponse transcriptionnelle plutôt que de la réduire. Cela se traduit par une épistasie importante au-delà des interactions par paires. Cela a des implications sur la façon dont ces réseaux évoluent. L'épistasie par paires que nous trouvons entre les facteurs de transcription globaux repose sur la présence ou l'absence d'autres perturbations. Cela implique que d'autres perturbations pourraient agir comme des mutations de potentialisation. Le nombre de voies d'évolution potentielles augmente avec les épistasies d'ordre élevé, même si cela ne nous dit rien sur la qualité de ces voies. Fait important, les répliques de cette thèse sont toujours en cours et les données présentées ici n’ont pas encore exclu les artefacts expérimentauxDespite advances in DNA sequencing, we have yet to understand how an organism’s phenotype relates to the contents of their genome. However it has become clear that the impact of genes are context dependant. The mere presence of a gene within a genome does not inform us of when it is expressed, and which other genes are expressed along with it. Understanding how gene expression is regulated is a necessary piece of understanding how phenotypes emerge from a given genotype. Transcription factors, which can activate or repress the expression of a gene, form a complex network of interactions between themselves and their targeted genes. This network consists of a hierarchy of groups of strongly connected transcription factors, each relating to distinct cellular processes. Is the structure of this transcriptional regulatory network significant to the transcriptional response of a cell? Here we use a programmable DNA binding protein called CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) to perturb gene expression of global regulators within the transcriptional regulatory network. These global regulators are regulating many distinct cellular processes and have many genetic targets. The CRISPR system allows us to perturb these regulators in all possible combinations, including higher order perturbations with potentially all targeted global regulators perturbed at the same time. We then record both the expression pattern of the transciptome using RNA sequencing, and the fitness of each strain. We find that the structure of the regulatory network increases the dimensionality of the transcriptional response rather than reducing it. This results in significant high order epistasis beyond pair-wise interactions. This has implications for how these networks evolve. The pair-wise epistasis we find between global transcription factors rely on the presence or absence of other perturbations. This implies that other perturbations could act as potentiating mutations. The number of potential evolutionary paths increases with high order epistasis, although this alone tells us nothing about the quality of those paths. Importantly, the replicates for this thesis are still on-going and the data presented here has not yet excluded experimental artefacts
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Pakka, Vijayanarasimha Hindupur. "Dynamics of molecular fluctuations in gene regulatory networks". Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/71823/.
Testo completoAbstract (sommario):
The components that are central to cellular processing are proteins, whose production is regulated by other proteins known as transcription factors. Proteins are products of genes that regulate the expression of one another, thereby forming large gene regulatory networks that perform specific cellular functions. The complex connectivity between genes of a network could result in various behaviours that are interesting. The assumption then is that tracking subnetwork behaviour helps understand the characteristics of the larger networks they are embedded in. For example, the structure of a subnetwork could say a lot about its biological role. Theoretical models of such systems and their deterministic dynamical properties have been the focus of study in the past. However, the dynamics of transcriptional control involves small numbers of molecules and result in significant fluctuations in protein and mRNA concentrations. Hence the recent shift in focus has been towards stochastic modelling approaches. Experimentally, the issues regarding average molecular numbers over a cell population draw our attention towards single-cell techniques where these fluctuations in the numbers are captured. On understanding the fluctuation properties of the smaller networks, one could study or design a combination of these networks leading to more complex regulatory networks. The objective of this thesis is to characterize small subnetworks of genes, based on the properties of their internal fluctuations. The correlations between these intrinsic fluctuations then offer, via the fluctuation dissipation relation, the possibility of capturing the system’s response to external perturbations, and hence the nature of the regulatory activity itself. Therefore we do a stochastic analysis and derive time-dependent noise correlation functions between molecular species of the networks, and using these functions we study simple networks by varying three of its factors. One is the type of regulatory activity that is present between two genes or proteins, whose correlations we are interested in. We show that the regulatory mechanism of activation, repression either by monomers or dimers, produces different correlations. We also study the dependence of the correlations on the values of the rate constants for the ingredient processes. We demonstrate the influence of various rate constants on the protein correlations. Finally, we analyze regulatory networks of different motifs such as cascades and feedforward loops and explore the extent to which fluctuation correlations report on the network structure. The distinct correlated fluctuations could then possibly be used as signatures for identifying the regulatory mechanism present between two genes of a network. To that end, in this thesis we present analytical and numerical results on features such as the magnitudes and time delays in dynamic correlations between proteins within smaller networks, and the dependence of these features on rate constants and regulatory and network mechanisms.
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Abedi, Vida. "System-identification of gene regulatory networks by systematic gene perturbation analysis". Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28254.
Testo completoAbstract (sommario):
Systems biology is an interdisciplinary field that combines engineering and molecular biology to better understand the 'design principles' of biological systems. One of the main goals in systems biology is to understand and map complex biological networks. In order to achieve this goal, tools able to process the non-linearity and high dimensionality of biological systems are urgently needed. To develop, test and benchmark tools for investigation of biological processes, it is important to utilize a model system that is able to provide a glimpse of complexity found in higher organisms, and be simple enough such that detailed studies can be performed rapidly, accurately, and reproducibly. Here, we have developed a system-identification framework for the extraction of quantitative and mechanistic information about causal relationship among genes using the canonical galactose utilization pathway in Saccharomyces cerevisiae. This framework, referred to as s&barbelow;ystematic g&barbelow;ene p&barbelow;erturbation a&barbelow;nalysis (SGPA), is based on the effects of systematic pair-wise gene deletions. In essence, the method establishes dynamical models of the regulatory network from single-cell measurements of steady-state input-output relationships, in systematically perturbed networks. SGPA framework is successful in identifying the network structure for the GAL system. This strategy leads the way to a better application of available resources and provides a scalable framework for system-identification and reverse-engineering of biological networks based on in vivo systematic data generation.
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Rodrigo, Tarrega Guillermo. "Computational design and designability of gene regulatory networks". Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/14179.
Testo completoAbstract (sommario):
Nuestro conocimiento de las interacciones moleculares nos ha conducido hoy hacia una perspectiva ingenieril, donde diseños e implementaciones de sistemas artificiales de regulación intentan proporcionar instrucciones fundamentales para la reprogramación celular. Nosotros aquí abordamos el diseño de redes de genes como una forma de profundizar en la comprensión de las regulaciones naturales. También abordamos el problema de la diseñabilidad dada una genoteca de elementos compatibles. Con este fin, aplicamos métodos heuríticos de optimización que implementan rutinas para resolver problemas inversos, así como herramientas de análisis matemático para estudiar la dinámica de la expresión genética. Debido a que la ingeniería de redes de transcripción se ha basado principalmente en el ensamblaje de unos pocos elementos regulatorios usando principios de diseño racional, desarrollamos un marco de diseño computacional para explotar este enfoque. Modelos asociados a genotecas fueron examinados para descubrir el espacio genotípico asociado a un cierto fenotipo. Además, desarrollamos un procedimiento completamente automatizado para diseñar moleculas de ARN no codificante con capacidad regulatoria, basándonos en un modelo fisicoquímico y aprovechando la regulación alostérica. Los circuitos de ARN resultantes implementaban un mecanismo de control post-transcripcional para la expresión de proteínas que podía ser combinado con elementos transcripcionales. También aplicamos los métodos heurísticos para analizar la diseñabilidad de rutas metabólicas. Ciertamente, los métodos de diseño computacional pueden al mismo tiempo aprender de los mecanismos naturales con el fin de explotar sus principios fundamentales. Así, los estudios de estos sistemas nos permiten profundizar en la ingeniería genética. De relevancia, el control integral y las regulaciones incoherentes son estrategias generales que los organismos emplean y que aquí analizamos.Rodrigo Tarrega, G. (2011). Computational design and designability of gene regulatory networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/14179
Palancia
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Rhodes, Johanna. "Identifying gene regulatory networks common to multiple plant stress responses". Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/56238/.
Testo completoAbstract (sommario):
Stress responses in plants can be defined as a change that affects the homeostasis of pathways, resulting in a phenotype that may or may not be visible to the human eye, affecting the fitness of the plant. Crosstalk is believed to be the shared components of pathways of networks, and is widespread in plants, as shown by examples of crosstalk between transcriptional regulation pathways, and hormone signalling. Crosstalk between stress responses is believed to exist, particularly crosstalk within the responses to biotic stress, and within the responses to abiotic stress. Certain hormone pathways are known to be involved in the crosstalk between the responses to both biotic and abiotic stresses, and can confer immunity or tolerance of Arabidopsis thaliana to these stresses. Transcriptional regulation has also been identified as an important factor in controlling tolerance and resistance to stresses. In this thesis, networks of regulation mediating the response tomultiple stresses are studied. Firstly, co-regulation was predicted for genes differentially expressed in two or more stresses by development of a novel multi-clustering approach, Wigwams Identifies Genes Working Across Multiple Stresses (Wigwams). This approach finds groups of genes whose expression is correlated within stresses, but also identifies a strong statistical link between subsets of stresses. Wigwams identifies the known co-expression of genes encoding enzymes of metabolic and flavonoid biosynthesis pathways, and predicts novels clusters of co-expressed genes. By hypothesising that by being coexpressed could also infer that the genes are co-regulated, promoter motif analysis and modelling provides information for potential upstream regulators. The context-free regulation of groups of co-expressed genes, or potential regulons, was explored using models generated by modelling techniques, in order to generate a quantitative model of transcriptional regulation during the response to B. cinerea, P. syringae pv. tomato DC3000 and senescence. This model was subsequently validated and extended by experimental techniques, using Yeast 1-Hybrid to investigate the protein-DNA interactions, and also microarrays. Analysis of mutants and plants overexpressing a predicted regulator, Rap2.6L, by gene expression analysis identified a number of potential regulon members as downstream targets. Rap2.6L was identified as an indirect regulator of the transcription factor members of three potential regulons co-expressed in the stresses B. cinerea, P. syringae pv. tomato DC3000 and long day senescence, allowing the confirmation of a predicted gene regulatory network operating in multiple stress responses.