Download or read book Computational Approaches to Understanding Transcription Regulation written by Paul Robert Munn and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transcription regulation refers to the coordination of numerous processes and protein complexes that results in the production of RNA from DNA. Disruption of the process of transcription has been implicated in disease and developmental disorders, but despite intense study, aspects of transcription regulation continue to remain elusive. Work described here attempts to provide computational approaches with which to further our understanding these events. Studies in Chapter Two investigate the relationship between chromatin structure and transcription regulation. To fully understand gene regulation, we need to understand the processes driving higher order chromatin organization. The high level, territorial structure of interphase chromatin is well established, as are the building blocks of chromatin, the nucleosomes, that form the 10 nm fibers. However, the chromatin folding process that gets us from this basic, 10 nm fiber to the high-level territories is less well understood. I investigate whether changes in chromatin organization are a factor in the response of a cell to stress. To study this, the cell was perturbed via heat-shock and the change in expression measured for selected genes known to respond to heat-shock. Chromatin conformation was then measured, using the Hi-C assay, before and after heat-shock, focusing on the interactions between the enhancers and promoters of these selected genes. Changes in structure that either increase or decrease interactions between specific regions of chromatin after stress was applied would be evidence for these changes driving gene expression. In Chapter Three, I explore machine learning approaches to more fully exploit available precision nuclear run-on and sequencing (PRO-seq) data to improve genome annotations. The start and extent of transcription is very specific. By sequencing RNA transcripts, or by measuring factors that correlate with transcription (such as modifications to histones, or regions in which chromatin is accessible) we can infer the position of elements such as enhancers or promoters. I hypothesized that PRO-seq signal contains subtle patterns that have not been leveraged extensively by previous methods. Here I use two different neural network architectures to see whether inferring patterns in the signal gives my methods an advantage.

Download or read book Computational Approaches to Understanding Gene Regulation written by Jacob Frank Degner and published by . This book was released on 2012 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: 3. Determine the extent to which chromatin accessibility and transcription-factor binding are involved in the mechanisms leading to differences in gene-expression levels among humans.

Download or read book Gene Regulation and Metabolism written by Julio Collado-Vides and published by MIT Press. This book was released on 2002 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of current computational approaches to metabolism and gene regulation.

Download or read book Computational Approaches to Understand Cell Type Specific Gene Regulation written by Shilu Zhang and published by . This book was released on 2021 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transcriptional regulatory networks are networks of regulatory proteins such as transcription factors, signaling protein level and chromatin modifications that together determine the transcriptional status of genes in different contexts such as cell types, diseases, and environmental conditions. Changes in regulatory networks can significantly alter the type or function of a cell. Therefore, identifying regulatory networks and determining how they transform over diverse cell types is key to understanding mammalian development and disease. In this dissertation, we have developed several computational methods to integrate regulatory genomic datasets such as chromatin marks, transcription factors and long-range regulatory interactions from multiple cell types to predict regulatory network connections and their dynamics.Our first contribution is HiC-Reg to predict long-range interactions in new cell types using one-dimensional regulatory genomic datasets such as chromatin marks, architectural and transcription factor proteins, and accessibility. Our second contribution is Cell type Varying Networks (CVN), a method to capture the interactions between chromatin marks, TFs and expression levels in each cell type on a lineage. Finally, we developed single-cell Multi-Task learning Network Inference (scMTNI), for inference of cell type-specific gene regulatory networks that leverages scRNA-seq and scATAC-seq measurements and captures the dynamic changes of networks across cell lineages. We applied these methods to simulated and real data, interpreted the results using existing literature, and provided biological insights for cell type-specific gene regulation. In particular, we identified network components that are common and differentially wired across the cellular stages that provide novel insight into network dynamics during reprogramming and hematopoietic differentiation. Taken together, we provide a powerful set of computational tools that integrate different omic datasets to infer cell type-specific regulatory networks which are applicable to different biological questions.

Download or read book Computational Modeling of Gene Regulatory Networks written by Hamid Bolouri and published by Imperial College Press. This book was released on 2008 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology.

Download or read book Systems Biology of Transcription Regulation written by Ekaterina Shelest and published by Frontiers Media SA. This book was released on 2016-09-09 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transcription regulation is a complex process that can be considered and investigated from different perspectives. Traditionally and due to technical reasons (including the evolution of our understanding of the underlying processes) the main focus of the research was made on the regulation of expression through transcription factors (TFs), the proteins directly binding to DNA. On the other hand, intensive research is going on in the field of chromatin structure, remodeling and its involvement in the regulation. Whatever direction we select, we can speak about several levels of regulation. For instance, concentrating on TFs, we should consider multiple regulatory layers, starting with signaling pathways and ending up with the TF binding sites in the promoters and other regulatory regions. However, it is obvious that the TF regulation, also including the upstream processes, represents a modest portion of all processes leading to gene expression. For more comprehensive description of the gene regulation, we need a systematic and holistic view, which brings us to the importance of systems biology approaches. Advances in methodology, especially in high-throughput methods, result in an ever-growing mass of data, which in many cases is still waiting for appropriate consideration. Moreover, the accumulation of data is going faster than the development of algorithms for their systematic evaluation. Data and methods integration is indispensable for the acquiring a systematic as well as a systemic view. In addition to the huge amount of molecular or genetic components of a biological system, the even larger number of their interactions constitutes the enormous complexity of processes occurring in a living cell (organ, organism). In systems biology, these interactions are represented by networks. Transcriptional or, more generally, gene regulatory networks are being generated from experimental ChIPseq data, by reverse engineering from transcriptomics data, or from computational predictions of transcription factor (TF) – target gene relations. While transcriptional networks are now available for many biological systems, mathematical models to simulate their dynamic behavior have been successfully developed for metabolic and, to some extent, for signaling networks, but relatively rarely for gene regulatory networks. Systems biology approaches provide new perspectives that raise new questions. Some of them address methodological problems, others arise from the newly obtained understanding of the data. These open questions and problems are also a subject of this Research Topic.

Download or read book Computational Approaches to Understand Transcriptional Regulation and Alternative Promoter Usage in Mammals written by Mette Jørgensen and published by . This book was released on 2014 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Predicting Transcription Factor Complexes written by Thorsten Will and published by Springer. This book was released on 2014-12-05 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: In his master thesis Thorsten Will proposes the substantial information content of protein complexes involving transcription factors in the context of gene regulatory networks, designs the first computational approaches to predict such complexes as well as their regulatory function and verifies the practicability using data of the well-studied yeast S.cereviseae. The novel insights offer extensive capabilities towards a better understanding of the combinatorial control driving transcriptional regulation.

Download or read book Modeling Transcriptional Regulation written by SHAHID MUKHTAR and published by Humana. This book was released on 2021-07-13 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides methods and techniques used in construction of global transcriptional regulatory networks in diverse systems, various layers of gene regulation and mathematical as well as computational modeling of transcriptional gene regulation. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Modeling Transcriptional Regulation: Methods and Protocols aims to provide an in depth understanding of new techniques in transcriptional gene regulation for specialized audience.

Download or read book Integrative Analysis of Transcriptional Regulation Using Computational Approaches written by Yili Chen and published by . This book was released on 2007 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advances in the Prediction and Understanding of Transcriptional Regulation in Yeast written by Timothy Edward Reddy and published by . This book was released on 2008 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Transcriptional regulation is at the very foundation of molecular biology. Governing the regulation is a complex code found in regions of the genome between the genes and much effort has gone into the study of the regulatory code. My work builds in depth and breadth by developing novel computational approaches to study known regulatory mechanisms, as well as by exploring unanswered questions about the function of genomic regulatory signals. Transcriptional regulation is carried out by a class of proteins known as transcription factors (TFs). TFs interact with regulatory sequences in the DNA and, in doing so, regulate transcription. Identification of the TF-bound regulatory sequences has long challenged computational biology. Here, I took a novel approach to study the behavior of a commonly used regulatory motif detection algorithm, Gibbs sampling. Based on the study, a series of new motif detection algorithms are developed that utilize high performance computing to better predict regulatory sequences. At the core of the developed algorithms is the concept that statistical sampling procedures are improved by observing the behavior of repeated application to the same dataset. Here, such ensemble approaches are used to identify regulatory sequences involved in a mammalian model of epilepsy. Additionally, combining the ensemble approach with graph theory significantly improves upon the ability of existing algorithms to predict yeast regulatory signals, and this work presents what is, to the best of my knowledge, the first application of graphically clustering Gibbs sampling results to predict regulatory sequences across a eukaryotic genome. It is well known that the function of regulatory sequences is modulated by aspects of the promoter beyond the binding site. However, current computational approaches do not consider aspects of the promoter outside the specific regulatory sequence thus limiting the utility of computational predictions. To address the problem, this work concludes with a case study of the role of promoter architecture in modulating the function of local regulatory sequences. The global sequence landscape appears to play a major and evolutionarily conserved role in modulating the function of otherwise well-studied local regulatory signals. The result suggests that new approaches to combine local and global sequence properties will better predict regulatory functions.

Download or read book Computational Approaches for Protein Functions and Gene Association Networks written by Hari Krishna Yalamanchili and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Computational Approaches for Protein Functions and Gene Association Networks" by Hari Krishna, Yalamanchili, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Entire molecular biology revolves primarily around proteins and genes (DNA and RNA). They collaborate with each other facilitating various biomolecular systems. Thus, to comprehend any biological phenomenon from very basic cell division to most complex cancer, it is fundamental to decode the functional dynamics of proteins and genes. Recently, computational approaches are being widely used to supplement traditional experimental approaches. However, each automated approach has its own advantages and limitations. In this thesis, major shortcomings of existing computational approaches are identified and alternative fast yet precise methods are proposed. First, a strong need for reliable automated protein function prediction is identified. Almost half of protein functional interpretations are enigmatic. Lack of universal functional vocabulary further elevates the problem. NRProF, a novel neural response based method is proposed for protein functional annotation. Neural response algorithm simulates human brain in classifying images; the same is applied here for classifying proteins. Considering Gene Ontology (GO) hierarchical structure as background, NRProF classifies a protein of interest to a specific GO category and thus assigns the corresponding function. Having established reliable protein functional annotations, protein and gene collaborations are studied next. Interactions amongst transcription factors (TFs) and transcription factor binding sites (TFBSs) are fundamental for gene regulation and are highly specific, even in evolution background. To explain this binding specificity a Co-Evo (co-evolutionary) relationship is hypothesized. Pearson correlation and Mutual Information (MI) metrics are used to validate the hypothesis. Residue level MI is used to infer specific binding residues of TFs and corresponding TFBSs, assisting a thorough understanding of gene regulatory mechanism and aid targeted gene therapies. After comprehending TF and TFBS associations, interplay between genes is abstracted as Gene Regulatory Networks. Several methods using expression correlations are proposed to infer gene networks. However, most of them ignore the embedded dynamic delay induced by complex molecular interactions and other riotous cellular mechanisms, involved in gene regulation. The delay is rather obvious in high frequency time series expression data. DDGni, a novel network inference strategy is proposed by adopting gapped smith-waterman algorithm. Gaps attune expression delays and local alignment unveils short regulatory windows, which traditional methods overlook. In addition to gene level expression data, recent studies demonstrated the merits of exon-level RNA-Seq data in profiling splice variants and constructing gene networks. However, the large number of exons versus small sample size limits their practical application. SpliceNet, a novel method based on Large Dimensional Trace is proposed to infer isoform specific co-expression networks from exon-level RNA-Seq data. It provides a more comprehensive picture to our understanding of complex diseases by inferring network rewiring between normal and diseased samples at isoform resolution. It can be applied to any exon level RNA-Seq data and exon array data. In summary, this thesis first identifies major shortcomings of existing computational approaches to functional association of proteins and genes, and develops seve

Download or read book Computational and Experimental Approaches to Understanding Mammalian Gene Regulation with Synthetic Biology written by Aditya Mukund and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transcriptional regulation is a key pathway cells use to regulate gene expression in response to temporal signaling, and is becoming widely used as a platform for synthetic biology applications. Transcription in mammalian cells is in large part driven by the actions of transcription factors, which consist of DNA-binding domains that recognize and bind to specific genomic locations and effector domains that recruit transcriptional machinery and chromatin regulators to activate or repress target genes. This work uses mathematical modeling and high-throughput experimental approaches to dissect the temporal and combinatorial logic of gene regulation in mammalian cells. First, we build a mathematical framework for analyzing the response of genetic circuits containing chromatin regulators to temporal signals in mammalian cell populations, elaborating on prior models in which individual cells stochastically transitioning between active, reversibly silent, and irreversibly silent gene states at constant rates over time. We analyze classical gene regulatory motifs such as feedforward and autoregulatory loops in the context of duration-dependent signaling, and find that repressive regulators with epigenetic memory can sum up and encode the total duration of their recruitment in the fraction of cells irreversibly silenced. Last, we use an information theoretic approach to show that all-or-none stochastic silencing can be used by populations to transmit information reliably and with high fidelity even in very simple genetic circuits. Second, we use a high-throughput approach to dissecting how distinct effector domains within a single transcription factor can be combined to regulate gene expression. We measure transcriptional activity for 8,400 effector domain combinations by recruiting them to reporter genes in human cells. We find that weak and moderate activation domains synergize to drive strong gene expression, while combining strong activators often results in weaker activation. In contrast, weaker repressor domains tend to average each other out, and moderate to strong repressor domains often overpower activation domains. We use this information to build a synthetic transcription factor whose function can be tuned between repression and activation independent of recruitment to target genes by using a small molecule drug. Altogether, this work helps to advance our understanding of how to build synthetic transcription factors, and how to design and model chromatin regulation-based circuits. Our hope is that these efforts will help advance mammalian synthetic biology as a tool for biological research and a reliable strategy for developing medical therapies.

Download or read book Computational Methods for Understanding the Molecular Mechanisms Governing Transcriptional Regulation in Humans written by Geoffrey John Macintyre and published by . This book was released on 2011 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Exploiting Network based Approaches for Understanding Gene Regulation and Function written by Sarath Chandra Janga and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: It is increasingly becoming clear in the post-genomic era that proteins in a cell do not work in isolation but rather work in the context of other proteins and cellular entities during their life time. This has lead to the notion that cellular components can be visualized as wiring diagrams composed of different molecules like proteins, DNA, RNA and metabolites. These systems-approaches for quantitatively and qualitatively studying the dynamic biological systems have provided us unprecedented insights at varying levels of detail into the cellular organization and the interplay between different processes. The work in this thesis attempts to use these systems or network-based approaches to understand the design principles governing different cellular processes and to elucidate the functional and evolutionary consequences of the observed principles. Chapter 1 is an introduction to the concepts of networks and graph theory summarizing the various properties which are frequently studied in biological networks along with an overview of different kinds of cellular networks that are amenable for graph-theoretical analysis, emphasizing in particular on transcriptional, post-transcriptional and functional networks. In Chapter 2, I address the questions, how and why are genes organized on a particular fashion on bacterial genomes and what are the constraints bacterial transcriptional regulatory networks impose on their genomic organization. I then extend this one step further to unravel the constraints imposed on the network of TF-TF interactions and relate it to the numerous phenotypes they can impart to growing bacterial populations. Chapter 3 presents an overview of our current understanding of eukaryotic gene regulation at different levels and then shows evidence for the existence of a higher-order organization of genes across and within chromosomes that is constrained by transcriptional regulation. The results emphasize that specific organization of genes across and within chromosomes that allowed for efficient control of transcription within the nuclear space has been selected during evolution. Chapter 4 first summarizes different computational approaches for inferring the function of uncharacterized genes and then discusses network-based approaches currently employed for predicting function. I then present an overview of a recent high-throughput study performed to provide a 'systems-wide' functional blueprint of the bacterial model, Escherichia coli K-12, with insights into the biological and evolutionary significance of previously uncharacterized proteins. In Chapter 5, I focus on post-transcriptional regulatory networks formed by RBPs. I discuss the sequence attributes and functional processes associated with RBPs, methods used for the construction of the networks formed by them and finally examine the structure and dynamics of these networks based on recent publicly available data. The results obtained here show that RBPs exhibit distinct gene expression dynamics compared to other class of proteins in a eukaryotic cell. Chapter 6 provides a summary of the important aspects of the findings presented in this thesis and their practical implications. Overall, this dissertation presents a framework which can be exploited for the investigation of interactions between different cellular entities to understand biological processes at different levels of resolution.

Download or read book Computational Annotations of Cell Type Specific Transcription Factors Binding and Long range Enhancer gene Interactions written by Wenjie Qi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precise execution of cell-type-specific gene transcription is critical for cell differentiation and development. The accurate lineage-specific gene regulation lies in the proper combinatorial binding of transcription factors (TFs) to the cis-regulatory elements. TFs bind to the proximal DNA sequences around the genes to exert control over gene transcription. Recently, experimental studies revealed that enhancers also recruit TFs to stimulate gene expression by forming long-range chromatin interactions, suggesting the interplay between gene, enhancer, and TFs in the 3D space in specifying cell fates. Identification of transcription factor binding sites (TFBSs) as well as pinpointing the long-range chromatin interactions is pivotal for understanding the transcriptional regulatory circuits. Experimental approaches have been developed to profile protein binding as well as 3D genome but have their limitations. Therefore, accurate and highly scalable computation methods are needed to comprehensively delineate the gene regulatory landscape. Accordingly, I have developed a supervised machine learning model, TF- wave, to predict TFBSs based on DNase-Seq data. By incorporating multi-resolutions features generated by applying Wavelet Transform to DNase-Seq data, TF-wave can accurately predict TFBSs at the genome-wide level in a tissue-specific way. I further designed a matrix factorization model, EP3ICO, to jointly infer enhancer-promoter interactions based on protein-protein interactions (PPIs) between TFs with combined orders. Compared with existing algorithms, EP3ICO not only identifies underlying mechanistic regulators that mediate the 3D chromatin interactions but also achieves superior performance in predicting long-range enhancer-promoter links. In conclusion, our models provide new computational approaches for profiling the cell-type specific TF bindings and high-resolution chromatin interactions.

Download or read book Computational Methods for Understanding Bacterial and Archaeal Genomes written by Ying Xu and published by World Scientific. This book was released on 2008 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 500 prokaryotic genomes have been sequenced to date, and thousands more have been planned for the next few years. While these genomic sequence data provide unprecedented opportunities for biologists to study the world of prokaryotes, they also raise extremely challenging issues such as how to decode the rich information encoded in these genomes. This comprehensive volume includes a collection of cohesively written chapters on prokaryotic genomes, their organization and evolution, the information they encode, and the computational approaches needed to derive such information. A comparative view of bacterial and archaeal genomes, and how information is encoded differently in them, is also presented. Combining theoretical discussions and computational techniques, the book serves as a valuable introductory textbook for graduate-level microbial genomics and informatics courses.