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MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression and play an essential role in phenotype development. The regulation mechanism behind miRNA reveals insight into gene expression and gene regulation. Transcription Start Site(TSS) is the key to studying gene expression. However, the TSSs of miRNAs can be thousands of nucleotides away from the precursor miRNAs, which makes it hard to be detected by conventional RNA-Seq experiments. Some previous methods tried to take advantage of sequencing data using sequence features or integrated epigenetic markers, but resulted in either not condition-specific or low-resolution prediction. Furthermore, the availability of a large amount of Single-Cell RNA-Seq(scRNA-Seq) data provides remarkable opportunities for studying gene regulatory mechanisms at single-cell resolution. Incorporating the gene regulatory mechanisms can assist with cell type identification and state discovery from scRNA-Seq data. In this dissertation, we studied computational modeling of gene transcription initialization and expression, including two novel approaches to identify TSSs with various type of conditions and one case study at the single-cell level. Firstly, we studied how TSS can be identified based on Cap Analysis Gene Expression (CAGE) experiments data using the thriving Deep Learning Neural Network. We used a control model to study the Deepbind binding score features that the protein binding motif model can improve overall prediction performance. Furthermore, comparing data from unseen cell lines showed better performance than existing tools. Secondly, to better predict the TSSs of miRNA in a condition-specific manner, we built D-miRT, a two-steam convolutional neural network based on integrated low-resolution epigenetic features and high-resolution sequence features. D-miRT outperformed all baseline models and demonstrated high accuracy for miRNA TSS prediction tasks. Compared with the most recent approaches on cell-specific miRNA TSS identification using cell lines that were unseen to the model training processes, D-miRT also showed superior performance. Thirdly, to study gene transcription initialization and regulation from single-cell perspective, we developed INSISTC, an unsupervised machine learning-based approach that incorporated network structure information for single-cell type classification. In contrast to other clustering algorithms, we showed that INSISTC with the SC3 algorithm provides cluster number estimation. Future studies on gene expression and regulation will benefit from INSISTC's adaptability with regard to the kinds of biological networks that can be used.
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./a
Introducing a handbook for gene regulatory network research using evolutionary computation, with applications for computer scientists, computational and system biologists This book is a step-by-step guideline for research in gene regulatory networks (GRN) using evolutionary computation (EC). The book is organized into four parts that deliver materials in a way equally attractive for a reader with training in computation or biology. Each of these sections, authored by well-known researchers and experienced practitioners, provides the relevant materials for the interested readers. The first part of this book contains an introductory background to the field. The second part presents the EC approaches for analysis and reconstruction of GRN from gene expression data. The third part of this book covers the contemporary advancements in the automatic construction of gene regulatory and reaction networks and gives direction and guidelines for future research. Finally, the last part of this book focuses on applications of GRNs with EC in other fields, such as design, engineering and robotics. • Provides a reference for current and future research in gene regulatory networks (GRN) using evolutionary computation (EC) • Covers sub-domains of GRN research using EC, such as expression profile analysis, reverse engineering, GRN evolution, applications • Contains useful contents for courses in gene regulatory networks, systems biology, computational biology, and synthetic biology • Delivers state-of-the-art research in genetic algorithms, genetic programming, and swarm intelligence Evolutionary Computation in Gene Regulatory Network Research is a reference for researchers and professionals in computer science, systems biology, and bioinformatics, as well as upper undergraduate, graduate, and postgraduate students. Hitoshi Iba is a Professor in the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, at the University of Tokyo, Toyko, Japan. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the journal of Genetic Programming and Evolvable Machines. Nasimul Noman is a lecturer in the School of Electrical Engineering and Computer Science at the University of Newcastle, NSW, Australia. From 2002 to 2012 he was a faculty member at the University of Dhaka, Bangladesh. Noman is an Editor of the BioMed Research International journal. His research interests include computational biology, synthetic biology, and bioinformatics.
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.
Gene expression is central to the identity and behavior of a cell. Expression is controlled at every stage of transcription and translation, but the first step is transcriptional initiation by RNA polymerase. A vast repertoire of special proteins, transcription factors (TFs), interact with DNA, RNA polymerase, and other proteins to regulate transcription. The gene regulatory networks that arise from TF-TF regulations form the fundamental basis for cellular behavior.
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.
Gene expression is the bridge between genetic information to biological function. Development of biotechnologies has enabled comprehensive profiling of gene expression, especially at the level of transcriptional regulation. The huge scale of data generated has reformed the way of biological studies and computational biology, or bioinformatics, analysis has become crucial. In this dissertation, I summarize three discoveries enabled by computational biology approaches. In the first project, I collaborated with experimental biologists and reported a previously unknown connection between enhancer transcription and gene regulation. In the second project, I proposed a novel analysis methodology that identified enhancer transcripts as targets to modulate gene expression. In the third project, I applied machine learning techniques that efficiently identified key cell types from heterogeneous samples by transcriptome profiling.
This volume focuses on modern computational and statistical tools for translational gene expression and regulation research to improve prognosis, diagnostics, prediction of severity, and therapies for human diseases. It introduces some of state of the art technologies as well as computational and statistical tools for translational bioinformatics in the areas of gene transcription and regulation, including the tools for next generation sequencing analyses, alternative spicing, the modeling of signaling pathways, network analyses in predicting disease genes, as well as protein and gene expression data integration in complex human diseases etc. The book is particularly useful for researchers and students in the field of molecular biology, clinical biology and bioinformatics, as well as physicians etc. Dr. Jiaqian Wu is assistant professor in the Vivian L. Smith Department of Neurosurgery and Center for Stem Cell and Regenerative Medicine, University of Texas Health Science Centre, Houston, TX, USA.​
Gene expression is an essential mechanism for physical and mental development of human. Aberrant regulation of gene expression creates abnormality in human body than can lead to complicated diseases. Gene expression can be regulated at any stage from the chromatin unfolding stage to post-translation stage of protein. In this study, we focused on two important factors of gene expression regulation that participate in the gene expression process at the transcription and the post-transcriptional stages; enhancer-promoter interactions and miRNA-mRNA interactions.