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Begins with an historical overview of the early discoveries surrounding DNA methylation in the mammalian brain and then explores the evidence supporting a role for this epigenetic mechanism in controlling gene expression programs across the lifespan in both normal and diseased states. Chapters describe new directions and technological advances, and provide an overview of what the future holds for this exciting new field. This book is ideal for medical, graduate and advanced undergraduate students, but is also a great resource for researchers who need a broad introduction to the dynamic nature of DNA that sheds light on evolving concepts of gene-environment interaction and their effects on adaptation and neuropsychiatric disease.--

Gene expression.

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Gene expression is dependent on multiple steps, including transcription, RNA processing, and translation. Importantly, recent studies revealed that gene expression is regulated by chromatin structures and non-coding RNA profiles. Elucidating the molecular mechanisms may contribute to the development of novel therapeutics for aging-related diseases, including cancer and neurodegenerative diseases. This book provides a comprehensive overview of gene expression and its role in human disease. It consists of nine chapters organized into two sections on molecular mechanisms in controlling gene expression and the relationships between transcriptional control and human disease.

Genetic regulation. --- Gene expression.

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Gene Expression --- Central Nervous System --- Central nervous system --- Gene expression --- Gene Expression. --- Gene expression. --- Genes --- Expression, Gene --- Expressions, Gene --- Gene Expressions --- Expression --- Genetic regulation --- Gene Expression Profiling

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Over the past decade, the transient gene expression (TGE) technology platform has been actively pursued to produce a wide range of therapeutic proteins, monoclonal antibodies, and vaccines for mainly preclinical assessment, due to its short development times and low overall cost. This book updates the latest advances in the field, with focusing on systematic description of the technology from cell lines, cell culture conditions, vector construction, expression strategy, current protocols, optimisation of the procedure, and potential for clinical application. As a conclusion, the author foresee

Gene expression. --- Genes --- Genetic regulation --- Expression

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Scientists today have access to an unprecedented arsenal of high-tech tools that can be used to thoroughly characterize biological systems of interest. High-throughput “omics” technologies enable to generate enormous quantities of data at the DNA, RNA, epigenetic and proteomic levels. One of the major challenges of the post-genomic era is to extract functional information by integrating such heterogeneous high-throughput genomic data. This is not a trivial task as we are increasingly coming to understand that it is not individual genes, but rather biological pathways and networks that drive an organism’s response to environmental factors and the development of its particular phenotype. In order to fully understand the way in which these networks interact (or fail to do so) in specific states (disease for instance), we must learn both, the structure of the underlying networks and the rules that govern their behavior. In recent years there has been an increasing interest in methods that aim to infer biological networks. These methods enable the opportunity for better understanding the interactions between genomic features and the overall structure and behavior of the underlying networks. So far, such network models have been mainly used to identify and validate new interactions between genes of interest. But ultimately, one could use these networks to predict large-scale effects of perturbations, such as treatment by multiple targeted drugs. However, currently, we are still at an early stage of comprehending methods and approaches providing a robust statistical framework to quantitatively assess the quality of network inference and its predictive potential. The scope of this Research Topic in Bioinformatics and Computational Biology aims at addressing these issues by investigating the various, complementary approaches to quantify the quality of network models. These “validation” techniques could focus on assessing quality of specific interactions, global and local structures, and predictive ability of network models. These methods could rely exclusively on in silico evaluation procedures or they could be coupled with novel experimental designs to generate the biological data necessary to properly validate inferred networks.

Validation --- Gene Expression --- Network Inference --- bioinformatics