Transcription factors (TFs) are biologically important proteins that interact with transcription machinery and bind DNA regulatory sequences to regulate gene expression by modulating the synthesis of the messenger RNA. The regulatory sequences comprise of short conserved regions of a specific length called motifs . TFs have very diverse roles in different cells and play a very significant role in development. TFs have been associated with carcinogenesis in various tissue types, as well as developmental and hormone response disorders. They may be responsible for the regulation of oncogenes and can be oncogenic. Consequently, understanding TF binding and knowing the motifs to which they bind is worthy of attention and research focus. Various projects have made the study of TF binding their main focus; nevertheless, much about TF binding remains confounding. Chromatin immunoprecipitation in conjunction with deep sequencing (ChIP-seq) techniques are a popular method used to investigate DNA-TF interactions in vivo. This procedure is followed by motif discovery and motif enrichment analysis using relevant tools. Protein Binding Microarrays (PBMs) are an in vitro method for investigating DNA-TF interactions. We use a motif enrichment analysis tools (CentriMo and AME) and an empirical quality assessment tool (Area under the ROC curve) to investigate which method yields motifs that are a true representation of in vivo binding. Motif enrichment analysis: On average, ChIP-seq derived motifs from the JASPAR Core database outperformed PBM derived ones from the UniPROBE mouse database. However, the performance of motifs derived using these two methods is not much different from each other when using CentriMo and AME. The E-values from Motif enrichment analysis were not too different from each other or 0. CentriMo showed that in 35 cases JASPAR Core ChIP-seq derived motifs outperformed UniPROBE mouse PBM derived motifs, while it was only in 11 cases that PBM derived motifs outperformed ChIP-seq derived motifs. AME showed that in 18 cases JASPAR Core ChIP-seq derived motifs did better, while only it was only in 3 cases that UniPROBE motifs outperformed ChIP-seq derived motifs. We could not distinguish the performance in 25 cases. Empirical quality assessment: Area under the ROC curve values computations followed by a two-sided t-test showed that there is no significant difference in the average performances of the motifs from the two databases (with 95% confidence, mean of differences=0.0088125 p-value= 0.4874, DF=47) .

Esta dissertação de mestrado tem como finalidade descrever o trabalho realizado em uma pesquisa que envolve a análise de expressões gênicas provenientes de microarrays com o objetivo de encontrar genes importantes em um organismo ou em uma determinada doença, como o câncer. Acreditamos que a descoberta desses genes, que chamamos aqui de genes de predição intrinsicamente multivariada (genes IMP), possa levar a descobertas de importantes processos biológicos ainda não conhecidos na literatura. A busca por genes IMP foi realizada em conjunto com estudos de modelos e conceitos matemáticos e estatísticos como redes Booleanas, cadeias de Markov, Coeficiente de Determinação (CoD), Classificação em análise de expressões gênicas e métodos de estimação de erro. No modelo de redes Booleanas, introduzido na Biologia por Kauffman, as expressões gênicas são quantizadas em apenas dois níveis: \"ligado\'\' ou \"desligado\'\'. O nível de expressão (estado) de cada gene, está relacionado com o estado de alguns outros genes através de uma função lógica. Adicionando uma perturbação aleatória a este modelo, temos um modelo mais geral conhecido como redes Booleanas com perturbação. O sistema dinâmico representado pela rede é uma cadeia de Markov ergódica e existe então uma distribuição de probabilidade estacionária. Temos a hipótese de que os experimentos de microarray seguem esta distribuição estacionária. O CoD é uma medida normalizada de quanto a expressão de um gene alvo pode ser melhor predita observando-se a expressão de um conjunto de genes preditores. Uma determinada configuração de CoDs caracteriza um gene alvo como sendo um gene IMP. Podemos trabalhar não somente com genes alvo, mas também com fenótipos alvo, onde o fenótipo de um sistema biológico poderia ser representado por uma variável aleatória binária. Por exemplo, podemos estar interessados em saber quais genes estão relacionados ao fenótipo de vida/morte de uma célula. Como a distribuição de probabilidade das amostras de microarray é desconhecida, o estudo dos CoDs é feito através de estimativas. Entre os métodos de estimação de erro estudados para este propósito podemos citar: Holdout, Resubstituição, Cross-validation, Bootstrap e .632 Bootstrap. Os métodos foram implementados para calcular os CoDs, permitindo então a busca por genes IMP. Os programas implementados na pesquisa foram usados em conjunto com uma pesquisa realizada pelo Prof. Dr. Hugo A. Armelin do Instituto de Química da USP. Este estudo em particular envolve a busca de genes importantes relacionados à morte de células tumorigênicas de camundongo disparada por FGF2 (Fibroblast Growth Factor 2). Nesta pesquisa observamos sub-redes de genes envolvidos no processo biológico em questão e também encontramos genes que podem estar relacionados ao fenômeno de morte das células de camundongo ou que estão, de fato, participando de alguma via disparada pelo FGF2. Esta abordagem de análise de expressões gênicas, juntamente com a pesquisa realizada pelo Prof. Armelin, resulta em uma metodologia para buscas de genes envolvidos em novos mecanismos de células tumorigênicas, ativados pelo FGF2. Na realidade esta metodologia pode ser aplicada em qualquer processo biológico de interesse científico, desde que seja possível modelar o problema proposto no contexto de redes Booleanas, coeficientes de determinação e genes IMP. / This Master\'s degree dissertation describes a research that involves an analysis of gene expression data from microarray experiments with the purpose to find important genes in certain organisms or diseases such as cancer. We believe that these type of genes, called intrinsically multivariately predictive genes (IMP genes), can lead to the discovery of important biological process that are unknown in the literature. The search for IMP genes was done with the study of mathematical and statistical models such as Boolean Networks, Markov Chains, Coefficient of Determination (CoD), Classification and Error Estimation Methods. In the Boolean network model, introduced in Biology by Kauffman, the gene expression is quantized in only two levels: ON and OFF. The expression level (state) of each gene is related with the state of some other genes through a logical function. Adding a random perturbation to this model, we have a more general Boolean-type model called Boolean network with perturbation. The dynamical system represented by this network is an ergodic Markov chain and thereby it possesses a steady-state distribution. We have the hypothesis that the microarray experiments follow this steady-state distribution. The CoD is a normalized measure of how much a gene expression of a target gene can be better predicted observing the expression of a set of predictor genes. A certain configuration of CoDs characterizes a target gene as an IMP gene. We can deal not only with target genes, but also with target phenotypes, where the phenotype of a biological system could be represented by a binary random variable. For example, we could be interested in knowing which genes are related to a life/death cell phenotype. Since the joint probability distribution of the gene expressions is unknown, the CoDs must be computed through estimated values. Among the error estimation methods studied we can cite: Holdout, Resubstitution, Cross-validation, Bootstrap and .632 Bootstrap. Those methods were implemented as a software in order to compute the CoDs and thereby allowing us to search for IMP genes. The software we implemented in this research was used within a research developed by Professor Dr. Hugo A. Armelin from the Instituto de Química - University of Sao Paulo. This particular research involves the search for important genes related to the death of tumorigenic mouse cells triggered by FGF2 (Fibroblast Growth Factor 2). From this research cooperation, we built some gene subnetworks involved in the target biological process and we found some genes that could be related to the death phenotype of mouse cells. This approach of gene expression analysis, together with the research developed by Professor Armelin, results in a methodology to search for important genes that could be involved in new mechanisms of tumorigenic cells triggered by FGF2. Actually, this methodology can be applied to any biological process of scientific interest, if one can model the proposed problem in the context of Boolean Networks, Coefficient of Determination and IMP genes.

Streptomycetes have been studied mostly as producers of antibiotics and for fundamentals of complex bacterial cell development. Here, transcriptomic and proteomic approaches were applied to systems study of Streptomyces coelicolor germination as a developmental transition from dormancy to the vegetative stage. The time dynamics of the gene expression levels represented by mRNA and intracellular protein accumulation and synthesis were measured throughout 5.5 h of germination at 13 time points by employing both DNA microarray and two-dimensional gel electrophoresis techniques. Using a numerical model of gene expression, genetic networks were reconstructed and functional groups of genes controlled by the sigma factors were identified. Modeling of the regulatory interactions provided a set of parameters allowing simulate kinetics of gene expression control among the sigma factors and their target genes. Particularly regulons of two sigma factors, SigR and HrdD, were identified. The analysis assigned their key role during the germination process. Analysis of global trends in the gene/protein expression revealed that the full capability of regulatory mechanisms responding to the environmental cues is reached within the first hour of germination, and identified the basic gene/protein functional groups...

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第18699号 / 情博第549号 / 新制||情||97(附属図書館) / 31632 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 阿久津 達也, 教授 鹿島 久嗣, 教授 石井 信 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Gene regulatory networks (GRNs) are central to every biological process from development to disease. GRNs are mediated through the activities of transcription factors (TFs), which interact in a sequence-specific manner with their target DNA elements to drive gene expression. In this thesis, two main aspects of GRNs are studied: (1) rewiring of GRNs by alternative TF isoforms, and (2) immune GRNs and strategies to modulate gene expression in immune diseases. TF isoforms resulting from alternative splicing, alternative transcription start sites, or alternative transcription termination sites, are prevalent and can have profound changes in GRNs. However, the extent to which differences in TF isoforms affect global GRNs and how such regulatory network rewiring leads to altered gene expression programs remain unclear. In this thesis, a large clone collection of ~800 human TF isoforms was generated, and then used in high-throughput systematic experimental strategies to investigate the extent to which TF isoforms differ at the level of molecular protein-DNA interactions (PDIs) and transcriptional regulatory activities. The findings show that at least half of alternative TF isoforms exhibit functional differences and tend to behave like distinct proteins with different molecular capabilities. In the context of global GRNs, these findings reveal a widespread expansion of PDI and transcriptional regulatory capabilities through alternative TF isoforms. Altogether, this work constitutes an important step towards the long-term goal of contextualizing and functionalizing large numbers of TF isoforms in rewiring GRNs. GRNs provide a wealth of information that can be leveraged in myriad ways including therapeutics. In particular, immune GRNs provide a framework for modulating cytokine gene expression, which are dysregulated in many human diseases. Proper cytokine gene expression is essential in development, homeostasis and immune responses. However, studies on the transcriptional control of cytokine genes over the last three decades have mostly focused on highly researched TFs and cytokines, resulting in an incomplete portrait of cytokine gene regulation. In this thesis, high-throughput assays were used to derive a comprehensive network that greatly expands the known repertoire of TF–cytokine gene PDIs and the set of TFs known to regulate cytokine genes. An enrichment of nuclear receptors was found and their role in cytokine regulation in primary macrophages was confirmed. Additionally, the network was used as a framework to identify TFs and synergistic TF pairs that can be targeted with FDA-approved drugs to modulate cytokine production. Finally, the PDI data was integrated with single cell RNA-seq datasets to identify druggable TF targets in cytokine-associated immune diseases (i.e., inflammatory bowel disease and COVID-19). Overall, this comprehensive cytokine GRN provides a rich resource to interrogate cytokine regulation in a variety of physiological and disease contexts. Altogether, the work in this thesis accomplishes the following: (1) identifies alternative TF isoforms as a major driver of GRN rewiring, (2) delineates a comprehensive cytokine GRN that greatly expands three decades of research, and (3) leverages the cytokine GRN to identify candidate therapeutic TF targets in diseases associated with dysregulated cytokine gene expression. These findings contribute a significant step in the effort to understand mechanisms of GRN rewiring and to generate comprehensive GRNs that provide a framework for modulating gene expression, particularly in diseases. / 2023-11-01T00:00:00Z

This paper reviews most frequently used computational modelling approaches and formal verification techniques in computational biology. The paper also compares a number of model checking tools and software suits used in analysing biological systems and biochemical networks and verifiying a wide range of biological properties.

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No description available.

Durante las últimas décadas el desarrollo de la bioinformática nos ha permitido lograr una mayor comprensión de los procesos biológicos que ocurren con nuestras células a nivel molecular. Al respecto, las mejoras e innovaciones en la tecnología continúan estimulando la mejora en la calidad de los datos biológicos que pueden ser obtenidos a nivel genómico. En tal sentido, grandes volúmenes de información pueden ser encontrados en formas de anotaciones o bases de datos computacionales. Estos conjuntos de datos, apropiadamente combinados, tienen el potencial de posibilitar descubrimientos novedosos que lleven a avances en campos tan relevantes para el desarrollo nacional como son la biotecnología o la medicina post-genómica. En particular, esta tesis se centra en la investigación de técnicas de aprendizaje automático y computación evolutiva para la inferencia de redes regulatorias de genes a partir de datos de expresión de genes, a nivel de genomas completos. Una red regulatoria de genes es una colección de segmentos de ADN (ácido desoxirribonucleico) en una célula que interactúan unos con otros (indirectamente a través del producto de su expresión) y con otras sustancias en la célula, gobernando así las tasas de transcripción de los genes de la red en ARNm (ácido ribonucleico mensajero). La principal contribución de esta tesis esta relacionada con el desarrollo de metodologías computacionales que asistan, a expertos en bioinformática, en la ingeniería inversa de las redes regulatorias de genes. En tal sentido, se desarrollaron algoritmos de computación evolutiva que permiten la identificación de grupos de genes co-expresados bajo ciertos subconjuntos de condiciones experimentales. Estos algoritmos se aplican sobre datos de expresión de genes, y optimizan características deseables desde el punto de vista biológico, posibilitando la obtención de relaciones de co-expresión relevantes. Tales algoritmos fueron cuidadosamente validados por medio de comparaciones con otras técnicas similares disponibles en la literatura, realizando estudios con datos reales y sintéticos a fin de mostrar la utilidad de la información extraída. Además, se desarrolló un algoritmo de inferencia que permite la extracción de potenciales relaciones causa-efecto entre genes, tanto simultáneas como también aquellas diferidas en el tiempo. Este algoritmo es una evolución de una técnica presentada con anterioridad, e incorpora características novedosas como la posibilidad de inferir reglas con múltiples retardos en el tiempo, a nivel genoma completo, e integrando múltiples conjuntos de datos. La técnica se validó mostrando su eficacia respecto de otros enfoques relevantes de la literatura. También se estudiaron los resultados obtenidos a partir de conjuntos de datos reales en términos de su relevancia biológica, exponiendo la viabilidad de la información inferida. Finalmente, estos algoritmos se integraron en una plataforma de software que facilita la utilización de estas técnicas permitiendo la inferencia, manipulación y visualización de redes regulatorias de genes. / In recent decades, the development of bioinformatics has allowed us to achieve a greater understanding of the biological processes that occur at the molecular level in our cells. In this regard, the improvements and innovations in technology continue to boost the improvement in the quality of the biological data that can be obtained at the genomic level. In this regard, large volumes of information can be found in forms of ontology's or computer databases. These datasets, appropriately combined, have the potential to enable novel discoveries that lead to progress in relevant fields to national development such as biotechnology and post-genomic medicine. In particular, this thesis focuses on the research of machine learning techniques and evolutionary computation for the inference of gene regulatory networks from gene expression data at genome-wide levels. A gene regulatory network is a collection of segments of DNA (deoxyribonucleic acid) in a cell which interact with each other (indirectly through their products of expression) and with other substances in the cell, thereby governing the rates of network genes transcription into mRNA (messenger ribonucleic acid). The main contribution of this thesis is related to the development of computational methodologies to attend experts in bioinformatics in the reverse engineering of gene regulatory networks. In this sense, evolutionary algorithms that allow the identification of groups of coexpressed genes under certain subsets of experimental conditions were developed. These algorithms are applied to gene expression data, and optimize desirable characteristics from the biological point of view, allowing the inference of relevant co-expression relationships. Such algorithms were carefully validated by the comparison with other similar techniques available in the literature, conducting studies with real and synthetic data in order to show the usefulness of the information extracted. Furthermore, an inference algorithm that allows the extraction of potential cause-effect relationships between genes, both simultaneous and time-delayed, were developed. This algorithm is an evolution of a previous approach, and incorporates new features such as the ability to infer rules with multiple time delays, at genome-wide level, and integrating multiple datasets. The technique was validated by showing its effectiveness over other relevant approaches in the literature. The results obtained from real datasets were also studied in terms of their biological relevance by exposing the viability of the inferred information. Finally, these algorithms were integrated into a software platform that facilitates the use of these techniques allowing the inference, manipulation and visualization of gene regulatory networks.

A gene regulatory network (GRN) records the interactions among transcription factors and their target genes. GRNs are useful to study how transcription factors (TFs) control gene expression as cells transition between states during differentiation and development. Scientists usually construct GRNs by careful examination and study of the literature. This process is slow and painstaking and does not scale to large networks. In this thesis, we study the problem of inferring GRNs automatically from gene expression data. Recent data-driven approaches to infer GRNs increasingly rely on single-cell level RNA-sequencing (scRNA-seq) data. Most of these methods rely on unsupervised or association based strategies, which cannot leverage known regulatory interactions by design. To facilitate supervised learning, we propose a novel graph convolutional neural network (GCN) based autoencoder to infer new regulatory edges from a known GRN and scRNA-seq data. As the name suggests, a GCN-based autoencoder consists of an encoder that learns a low-dimensional embedding of the nodes (genes) in the input graph (the GRN) through a series of graph convolution operations and a decoder that aims to reconstruct the original graph as accurately as possible. We investigate several GCN-based architectures to determine the ideal encoder-decoder combination for GRN reconstruction. We systematically study the performance of these and other supervised learning methods on different mouse and human scRNA-seq datasets for two types of evaluation. We demonstrate that our GCN-based approach substantially outperforms traditional machine learning approaches. / Master of Science / In multi-cellular living organisms, stem cells differentiate into multiple cell types. Proteins called transcription factors (TFs) control the activity of genes to effect these transitions. It is possible to represent these interactions abstractly using a gene regulatory network (GRN). In a GRN, each node is a TF or a gene and each edge connects a TF to a gene or TF that it controls. New high-throughput technologies that can measure gene expression (activity) in individual cells provide rich data that can be used to construct GRNs. In this thesis, we take advantage of recent advances in the field of machine learning to develop a new computational method for computationally constructing GRNs. The distinguishing property of our technique is that it is supervised, i.e., it uses experimentally-known interactions to infer new regulatory connections. We investigate several variations of this approach to reconstruct a GRN as close to the original network as possible. We analyze and provide a rationale for the decisions made in designing, evaluating, and choosing the characteristics of our predictor. We show that our predictor has a reconstruction accuracy that is superior to other supervised-learning approaches.