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Seleção de características e predição intrinsecamente multivariada em identificação de redes de regulação gênica; Feature selection and intrinsically multivariate prediction in gene regulatory networks identification

Martins Junior, David Corrêa
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 01/12/2008 Português
Relevância na Pesquisa
98.03863%
Seleção de características é um tópico muito importante em aplicações de reconhecimento de padrões, especialmente em bioinformática, cujos problemas são geralmente tratados sobre um conjunto de dados envolvendo muitas variáveis e poucas observações. Este trabalho analisa aspectos de seleção de características no problema de identificação de redes de regulação gênica a partir de sinais de expressão gênica. Particularmente, propusemos um modelo de redes gênicas probabilísticas (PGN) que devolve uma rede construída a partir da aplicação recorrente de algoritmos de seleção de características orientados por uma função critério baseada em entropia condicional. Tal critério embute a estimação do erro por penalização de amostras raramente observadas. Resultados desse modelo aplicado a dados sintéticos e a conjuntos de dados de microarray de Plasmodium falciparum, um agente causador da malária, demonstram a validade dessa técnica, tendo sido capaz não apenas de reproduzir conhecimentos já produzidos anteriormente, como também de produzir novos resultados. Outro aspecto investigado nesta tese é o fenômeno da predição intrinsecamente multivariada (IMP), ou seja, o fato de um conjunto de características ser um ótimo caracterizador dos objetos em questão...

Canalização: fenótipos robustos como consequência de características da rede de regulação gênica; Canalization: phenotype robustness as consequence of characteristics of the gene regulatory network

Patricio, Vitor Hugo Louzada
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 20/04/2011 Português
Relevância na Pesquisa
78.20182%
Em sistemas biológicos, o estudo da estabilidade das redes de regulação gênica é visto como uma contribuição importante que a Matemática pode proporcionar a pesquisas sobre câncer e outras doenças genéticas. Neste trabalho, utilizamos o conceito de ``canalização'' como sinônimo de estabilidade em uma rede biológica. Como as características de uma rede de regulação canalizada ainda são superficialmente compreendidas, estudamos esse conceito sob o ponto de vista computacional: propomos um modelo matemático simplificado para descrever o fenômeno e realizamos algumas análises sobre o mesmo. Mais especificamente, a estabilidade da maior bacia de atração das redes Booleanas - um clássico paradigma para a modelagem de redes de regulação - é analisada. Os resultados indicam que a estabilidade da maior bacia de atração está relacionada com dados biológicos sobre o crescimento de colônias de leveduras e que considerações sobre a interação entre as funções Booleanas e a topologia da rede devem ser realizadas conjuntamente na análise de redes estáveis.; In biological systems, the study of gene regulatory networks stability is seen as an important contribution that Mathematics can make to cancer research and that of other genetic diseases. In this work...

Inferência de redes de regulação gênica utilizando o paradigma de crescimento de sementes; Inference of gene regulatory networks using the seed growing paradigm

Higa, Carlos Henrique Aguena
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 17/02/2012 Português
Relevância na Pesquisa
98.22109%
Um problema importante na área de Biologia Sistêmica é o de inferência de redes de regulação gênica. Os avanços científicos e tecnológicos nos permitem analisar a expressão gênica de milhares de genes simultaneamente. Por "expressão gênica'', estamos nos referindo ao nível de mRNA dentro de uma célula. Devido a esta grande quantidade de dados, métodos matemáticos, estatísticos e computacionais têm sido desenvolvidos com o objetivo de elucidar os mecanismos de regulação gênica presentes nos organismos vivos. Para isso, modelos matemáticos de redes de regulação gênica têm sido propostos, assim como algoritmos para inferir estas redes. Neste trabalho, focamos nestes dois aspectos: modelagem e inferência. Com relação à modelagem, estudamos modelos existentes para o ciclo celular da levedura (Saccharomyces cerevisiae). Após este estudo, propomos um modelo baseado em redes Booleanas probabilísticas sensíveis ao contexto, e em seguida, um aprimoramento deste modelo, utilizando cadeias de Markov não homogêneas. Mostramos os resultados, comparando os nossos modelos com os modelos estudados. Com relação à inferência, propomos um novo algoritmo utilizando o paradigma de crescimento de semente de genes. Neste contexto...

Exploring ensemble learning techniques to optimize the reverse engineering of gene regulatory networks; Explorando técnicas de ensemble learning para otimizar a engenharia reversa de redes regulatórias genéticas

Mendoza, Mariana Recamonde
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Tese de Doutorado Formato: application/pdf
Português
Relevância na Pesquisa
98.29321%
In this thesis we are concerned about the reverse engineering of gene regulatory networks from post-genomic data, a major challenge in Bioinformatics research. Gene regulatory networks are intricate biological circuits responsible for govern- ing the expression levels (activity) of genes, thereby playing an important role in the control of many cellular processes, including cell differentiation, cell cycle and metabolism. Unveiling the structure of these networks is crucial to gain a systems- level understanding of organisms development and behavior, and eventually shed light on the mechanisms of diseases caused by the deregulation of these cellular pro- cesses. Due to the increasing availability of high-throughput experimental data and the large dimension and complexity of biological systems, computational methods have been essential tools in enabling this investigation. Nonetheless, their perfor- mance is much deteriorated by important computational and biological challenges posed by the scenario. In particular, the noisy and sparse features of biological data turn the network inference into a challenging combinatorial optimization prob- lem, to which current methods fail in respect to the accuracy and robustness of predictions. This thesis aims at investigating the use of ensemble learning tech- niques as means to overcome current limitations and enhance the inference process by exploiting the diversity among multiple inferred models. To this end...

Gene regulatory networks for development

Levine, Michael; Davidson, Eric H.
Fonte: National Academy of Sciences Publicador: National Academy of Sciences
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
97.98014%
The genomic program for development operates primarily by the regulated expression of genes encoding transcription factors and components of cell signaling pathways. This program is executed by cis-regulatory DNAs (e.g., enhancers and silencers) that control gene expression. The regulatory inputs and functional outputs of developmental control genes constitute network-like architectures. In this PNAS Special Feature are assembled papers on developmental gene regulatory networks governing the formation of various tissues and organs in nematodes, flies, sea urchins, frogs, and mammals. Here, we survey salient points of these networks, by using as reference those governing specification of the endomesoderm in sea urchin embryos and dorsal–ventral patterning in the Drosophila embryo.

Modular Evolution of DNA-Binding Preference of a Tbrain Transcription Factor Provides a Mechanism for Modifying Gene Regulatory Networks

Cheatle Jarvela, Alys M.; Brubaker, Lisa; Vedenko, Anastasia; Gupta, Anisha; Armitage, Bruce A.; Bulyk, Martha L.; Hinman, Veronica F.
Fonte: Oxford University Press Publicador: Oxford University Press
Tipo: Artigo de Revista Científica
Português
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Gene regulatory networks (GRNs) describe the progression of transcriptional states that take a single-celled zygote to a multicellular organism. It is well documented that GRNs can evolve extensively through mutations to cis-regulatory modules (CRMs). Transcription factor proteins that bind these CRMs may also evolve to produce novelty. Coding changes are considered to be rarer, however, because transcription factors are multifunctional and hence are more constrained to evolve in ways that will not produce widespread detrimental effects. Recent technological advances have unearthed a surprising variation in DNA-binding abilities, such that individual transcription factors may recognize both a preferred primary motif and an additional secondary motif. This provides a source of modularity in function. Here, we demonstrate that orthologous transcription factors can also evolve a changed preference for a secondary binding motif, thereby offering an unexplored mechanism for GRN evolution. Using protein-binding microarray, surface plasmon resonance, and in vivo reporter assays, we demonstrate an important difference in DNA-binding preference between Tbrain protein orthologs in two species of echinoderms, the sea star, Patiria miniata, and the sea urchin...

Gene regulatory netwok reconstrution by bayesian integration of prior knowledge and/ordifferent experimental conditions

Werhli, Adriano Velasques; Husmeier, Dirk
Fonte: Universidade Federal do Rio Grande Publicador: Universidade Federal do Rio Grande
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
88.0815%
There have been various attempts to improve the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach is based on pioneering work by Imoto et al.11 where the prior knowledge is expressed in terms of energy functions, from which a prior distribution over network structures is obtained in the form of a Gibbs distribution. The hyperparameters of this distribution represent the weights associated with the prior knowledge relative to the data. We have derived and tested a Markov chain Monte Carlo (MCMC) scheme for sampling networks and hyperparameters simultaneously from the posterior distribution, thereby automatically learning how to trade off information from the prior knowledge and the data. We have extended this approach to a Bayesian coupling scheme for learning gene regulatory networks from a combination of related data sets, which were obtained under different experimental conditions and are therefore potentially associated with different active subpathways. The proposed coupling scheme is a compromise between (1) learning networks from the different subsets separately, whereby no information between the different experiments is shared; and (2) learning networks from a monolithic fusion of the individual data sets...

Inferring microRNA and transcription factor regulatory networks in heterogeneous data

Le, T.; Liu, L.; Liu, B.; Tsykin, A.; Goodall, G.; Satou, K.; Li, J.
Fonte: BioMed Central Ltd. Publicador: BioMed Central Ltd.
Tipo: Artigo de Revista Científica
Publicado em //2013 Português
Relevância na Pesquisa
88.38379%
Background: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. Results: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then...

Phenotype accessibility and noise in random threshold gene regulatory networks

Pinho, Ricardo; Garcia, Victor; Feldman, Marcus W
Fonte: PLOS Publicador: PLOS
Tipo: Artigo de Revista Científica
Publicado em 28/04/2014 Português
Relevância na Pesquisa
78.32569%
Evolution requires phenotypic variation in a population of organisms for selection to function. Gene regulatory processes involved in organismal development affect the phenotypic diversity of organisms. Since only a fraction of all possible phenotypes are predicted to be accessed by the end of development, organisms may evolve strategies to use environmental cues and noise-like fluctuations to produce additional phenotypic diversity, and hence to enhance the speed of adaptation. We used a generic model of organismal development --gene regulatory networks-- to investigate how different levels of noise on gene expression states (i.e. phenotypes) may affect access to new, unique phenotypes, thereby affecting phenotypic diversity. We studied additional strategies that organisms might adopt to attain larger phenotypic diversity: either by augmenting their genome or the number of gene expression states. This was done for different types of gene regulatory networks that allow for distinct levels of regulatory influence on gene expression or are more likely to give rise to stable phenotypes. We found that if gene expression is binary, increasing noise levels generally decreases phenotype accessibility for all network types studied. If more gene expression states are considered...

A Computational Model Inspired by Gene Regulatory Networks

Lopes, Rui Miguel Lourenço
Fonte: Universidade de Coimbra Publicador: Universidade de Coimbra
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
87.98014%
Evolutionary Algorithms (EA) are parallel stochastic search procedures that are loosely inspired by the concepts of natural selection and genetic heredity. They have been successfully applied to many domains, and today Evolutionary Computation (EC) attracts a growing number of researchers from the most varied fields. The end of the 20th century brought uncountable discoveries in the biological realm, enabled by the underlying technological breakthroughs. Complete genomes have been sequenced, including the human one, and thanks to the increasing interdisciplinarity of researchers it is known today that there is much more to evolution than just natural selection, namely the influence of the environment, gene regulation, and development. At the core of these processes there is a fundamental piece of complex biological machinery, the Genetic Regulatory Network (GRN). This network results from the interaction amongst the genes and proteins, as well as the environment, governing gene expression and consequently the development of the organism. It is a true fact that the biological knowledge has advanced faster than our ability to incorporate it into the EAs, despite of whether or not it is benificial to do so. One of the main critics pointed-out is that the approach to the genotype-phenotype relationship is different from nature. A lot of effort has been put by some researchers into developing new representations...

Minimal gene regulatory circuits for a lysis-lysogeny choice in the presence of noise

Avlund, M.; Krishna, S.; Semsey, S.; Dodd, I.; Sneppen, K.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em //2010 Português
Relevância na Pesquisa
87.77219%
Gene regulatory networks (GRNs) that make reliable decisions should have design features to cope with random fluctuations in the levels or activities of biological molecules. The phage GRN makes a lysis-lysogeny decision informed by the number of phages infecting the cell. To analyse the design of decision making GRNs, we generated random in silico GRNs comprised of two or three transcriptional regulators and selected those able to perform a -like decision in the presence of noise. Various two-protein networks analogous to the CI-Cro GRN worked in noise-less conditions but failed when noise was introduced. Adding a CII-like protein significantly improved robustness to noise. CII relieves the CI-like protein of its ‘decider’ function, allowing CI to be optimized as a decision ‘maintainer’. CII's lysogenic decider function was improved by its instability and rapid removal once the decision was taken, preventing its interference with maintenance. A more reliable decision also resulted from simulated co-transcription of the genes for CII and the Cro-like protein, which correlates fluctuations in these opposing decider functions and makes their ratio less noisy. Thus, the decision network contains design features for reducing and resisting noise.; Mikkel Avlund...

System Identification Methods For Reverse Engineering Gene Regulatory Networks

WANG, ZHEN
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
98.181045%
With the advent of high throughput measurement technologies, large scale gene expression data are available for analysis. Various computational methods have been introduced to analyze and predict meaningful molecular interactions from gene expression data. Such patterns can provide an understanding of the regulatory mechanisms in the cells. In the past, system identification algorithms have been extensively developed for engineering systems. These methods capture the dynamic input/output relationship of a system, provide a deterministic model of its function, and have reasonable computational requirements. In this work, two system identification methods are applied for reverse engineering of gene regulatory networks. The first method is based on an orthogonal search; it selects terms from a predefined set of gene expression profiles to best fit the expression levels of a given output gene. The second method consists of a few cascades, each of which includes a dynamic component and a static component. Multiple cascades are added in a parallel to reduce the difference of the estimated expression profiles with the actual ones. Gene regulatory networks can be constructed by defining the selected inputs as the regulators of the output. To assess the performance of the approaches...

Reduction of the chemical master equation for gene regulatory networks using proper generalized decompositions

AMMAR, Amine; CUETO, Elias; CHINESTA, Francisco
Fonte: Wiley Library Publicador: Wiley Library
Português
Relevância na Pesquisa
97.9271%
The numerical solution of the chemical master equation (CME) governing gene regulatory networks and cell signaling processes remains a challenging task owing to its complexity, exponentially growing with the number of species involved. Although most of the existing techniques rely on the use of Monte Carlo-like techniques, we present here a new technique based on the approximation of the unknown variable (the probability of having a particular chemical state) in terms of a finite sum of separable functions. In this framework, the complexity of the CME grows only linearly with the number of state space dimensions. This technique generalizes the so-called Hartree approximation, by using terms as needed in the finite sums decomposition for ensuring convergence. But noteworthy, the ease of the approximation allows for an easy treatment of unknown parameters (as is frequently the case when modeling gene regulatory networks, for instance). These unknown parameters can be considered as new space dimensions. In this way, the proposed method provides solutions for any value of the unknown parameters (within some interval of arbitrary size) in one execution of the program.; Spanish Ministry of Science and Innovation (CICTY-DPI2011-27778-C02-010)

Majority rules with random tie-breaking in Boolean gene regulatory networks

Chaouiya, Claudine; Ourrad, Ouerdia; Lima, Ricardo
Fonte: PLOS Publicador: PLOS
Tipo: Artigo de Revista Científica
Publicado em 26/07/2013 Português
Relevância na Pesquisa
97.93204%
We consider threshold boolean gene regulatory networks, where the update function of each gene is described as a majority rule evaluated among the regulators of that gene: it is turned ON when the sum of its regulator contributions is positive (activators contribute positively whereas repressors contribute negatively) and turned OFF when this sum is negative. In case of a tie (when contributions cancel each other out), it is often assumed that the gene keeps it current state. This framework has been successfully used to model cell cycle control in yeast. Moreover, several studies consider stochastic extensions to assess the robustness of such a model. Here, we introduce a novel, natural stochastic extension of the majority rule. It consists in randomly choosing the next value of a gene only in case of a tie. Hence, the resulting model includes deterministic and probabilistic updates. We present variants of the majority rule, including alternate treatments of the tie situation. Impact of these variants on the corresponding dynamical behaviours is discussed. After a thorough study of a class of two-node networks, we illustrate the interest of our stochastic extension using a published cell cycle model. In particular, we demonstrate that steady state analysis can be rigorously performed and can lead to effective predictions; these relate for example to the identification of interactions whose addition would ensure that a specific state is absorbing.

LegumeGRN: a gene regulatory network prediction server for functional and comparative studies

Wang, Mingyi; Verdier, Jerome; Benedito, Vagner A; Tang, Yuhong; Murray, Jeremy D; Ge, Yinbing; Becker, Jörg D; Carvalho, Helena; Rogers, Christian; Udvardi, Michael; He, Ji
Fonte: PLOS Publicador: PLOS
Tipo: Artigo de Revista Científica
Publicado em 03/07/2013 Português
Relevância na Pesquisa
87.80926%
Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm...

Redes de regulação gênica do metabolismo de sacarose em cana-de-açúcar utilizando redes bayesianas; Gene regulatory networks of the sucrose metabolism in sugarcane using bayesian networks

Natália Faraj Murad
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 20/02/2013 Português
Relevância na Pesquisa
88.2067%
A cana-de-açúcar é uma das mais importantes plantas cultivadas no Brasil, que é o maior produtor e exportador mundial. Seu valor econômico é devido principalmente a sua capacidade de estocar sacarose nos colmos. Os padrões de expressão gênica podem regular processos de desenvolvimento da planta e influenciar no acúmulo de sacarose em tecidos de reserva. A regulação desses padrões ocorre através de complexos sistemas de interações entre muitos genes e seus produtos, resultando em uma complexa rede de regulação gênica. Modelos gráficos probabilísticos têm sido amplamente utilizados para inferência e representação dessas redes. Dentre eles, as redes bayesianas são o principal por ser considerado o método mais flexível e também requererem um número reduzido de parâmetros para a descrição do modelo. Sendo assim, este estudo utilizou a metodologia de redes bayesianas para inferência de interações regulatórias entre genes de metabolismo e sinalização de sacarose a partir de dados de expressão gênica, obtidos através de microarrays, disponíveis no Gene Expression Omnibus (GEO). As redes foram obtidas através de softwares para inferência de redes e então analisadas quanto aos genes que as compõem e padrões de expressão. Os genes foram agrupados em clusters considerando-se seus padrões de coexpressão. Os genes mais representados no cluster da enzima sacarose fosfato sintase (SPS) em cana são genes de relacionados à tradução...

A Solver for the Stochastic Master Equation Applied to Gene Regulatory Networks

Hegland, Markus; Burden, Conrad; Santoso, Lucia; MacNamara, Shevarl; Booth, Hilary
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
97.9271%
An important driver of gene regulatory networks is noise arising from the stochastic nature of interactions of genes, their products and regulators. Thus, such systems are stochastic and can be modelled by the chemical master equations. A major challenge

Motifs emerge from function in model gene regulatory networks

Burda, Z.; Krzywicki, A.; Martin, O. C.; Zagorski, M.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/04/2011 Português
Relevância na Pesquisa
78.15672%
Gene regulatory networks arise in all living cells, allowing the control of gene expression patterns. The study of their topology has revealed that certain subgraphs of interactions or "motifs" appear at anomalously high frequencies. We ask here whether this phenomenon may emerge because of the functions carried out by these networks. Given a framework for describing regulatory interactions and dynamics, we consider in the space of all regulatory networks those that have a prescribed function. Monte Carlo sampling is then used to determine how these functional networks lead to specific motif statistics in the interactions. In the case where the regulatory networks are constrained to exhibit multi-stability, we find a high frequency of gene pairs that are mutually inhibitory and self-activating. In contrast, networks constrained to have periodic gene expression patterns (mimicking for instance the cell cycle) have a high frequency of bifan-like motifs involving four genes with at least one activating and one inhibitory interaction.; Comment: 10 pages, 5 figures

Bifurcation Analysis of Gene Regulatory Circuits Subject to Copy Number Variation;

Mileyko, Yuriy
Fonte: SIAM PUBLICATIONS Publicador: SIAM PUBLICATIONS
Publicado em //2010 Português
Relevância na Pesquisa
78.15895%
Gene regulatory networks are comprised of many small gene circuits. Understanding expression dynamics of gene circuits for broad ranges of parameter space may provide insight into the behavior of larger regulatory networks as well as facilitate the use of circuits as autonomous units performing specific regulatory tasks. In this paper, we consider three common gene circuits and investigate the dependence of gene expression dynamics on the circuit copy number. In particular, we perform a detailed bifurcation analysis of the circuits' corresponding nonlinear gene regulatory models restricted to protein-only dynamics. Employing a geometric approach to bifurcation theory, we are able to derive closed form expressions for conditions which guarantee existence of saddle-node bifurcations caused by variation in the circuit copy number or copy number concentration. This result shows that the drastic effect of copy number variation on equilibrium behavior of gene circuits is highly robust to variation in other parameters in the circuits. We discuss a possibility of extending the current results to higher dimensional models which incorporate more details of the gene regulatory process.

A hybrid approach to building gene regulatory networks with Bayesian inference

Sari, Alparslan
Fonte: University of Delaware Publicador: University of Delaware
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
88.33567%
Liao, Li; Gene regulation plays a central role in cell biology. High throughput technologies, such as DNA microarray and next generation sequencing, enable measurement of gene expression at large scale, and this makes it possible to study gene regulation at the network level. Over the past decade, many computational methods have been developed to analyze the colossal amounts of gene expression data to infer gene regulatory network. The recent discovery of microRNA as regulatory elements represents opportunities and challenges to re-examine gene regulation. In this study, we developed a hybrid approach to construct regulatory gene network by incorporating prior knowledge including microRNAs as regulatory elemets. The hybrid approach, with use of Bayesian networks, combines learning without prior knowledge and using a predefined partial network to start the learning process in order to build a well-defined, more complete regulatory network. Existing methods either learn a network from scratch or use a predefined/complete network to just learn network's parameters. We used predefined partial networks and other prior knowledge (protein-protein interactions and transcription factor information) as constraints, and used a Bayesian network to infer new edges for a more complete network. Specifically...