This volume aims to provide a timely view of the state-of-the-art in systems biology. The editors take the opportunity to define systems biology as they and the contributing authors see it, and this will lay the groundwork for future studies. The volume is well-suited to both students and researchers interested in the methods of systems biology. Although the focus is on plant systems biology, the proposed material could be suitably applied to any organism.
This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians.
This volume presents protocols that analyze and explore gene regulatory networks (GRNs) at different levels in plants. This book is divided into two parts: Part I introduces different experimental techniques used to study genes and their regulatory interactions in plants. Part II highlights different computational approaches used for the integration of experimental data and bioinformatics-based predictions of regulatory interactions. This part of the book also provides information on essential database resources that grant access to gene-regulatory and molecular interactions in different plant genomes, with a specific focus on Arabidopsis thaliana. 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. Thorough and cutting-edge, Plant Gene Regulatory Networks: Methods and Protocols is a valuable resource for scientists and researchers interested in expanding their knowledge of GRNs.
This second edition volume discusses the revolutionary development of faster and less expensive DNA sequencing technologies from the past 10 years and focuses on general technologies that can be utilized by a wide array of plant biologists to address specific questions in their favorite model systems. This book is organized into five parts. Part I examines the tools and methods required for identifying epigenetic and conformational changes at the whole-genome level. Part II presents approaches used to determine key aspects of a gene’s function, such as techniques used to identify and characterize gene regulatory networks. This is followed by a discussion of tools used to analyze the levels of mRNA, mRNA translation rates and metabolites. Part III features a compilation of forward and reverse genetic approaches that include recent implementation of high-throughput sequencing in classical methodologies such as QTL mapping. The final two parts explore strategies to facilitate and accelerate the generation and testing of functional DNA elements and basic computational tools used to facilitate the use of systems biology approached by a broad spectrum of plant researchers. Written in the highly successful Methods of 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 key tips on troubleshooting and avoiding known pitfalls. Practical and timely, Plant Functional Genomics: Methods and Protocols, Second Edition highlights the latest developments in DNA sequencing technologies that are likely to continue shaping the future of functional genomics.
Systems Immunology and Infection Microbiology provides a large amount of biological system models, diagrams and flowcharts to illustrate development procedures and help users understand the results of systems immunology and infection microbiology. Chapters discuss systems immunology, systems infection microbiology, systematic inflammation and immune responses in restoration and regeneration process, systems' innate and adaptive immunity in infection process, systematic genetic and epigenetic pathogenic/defensive mechanism during bacterial infection on human cells is introduced, and the systematic genetic and epigenetic pathogenic/defensive mechanisms during viral infection on human cells. This book provides new big data-driven and systems-driven systems immunology and infection microbiology to researchers applying systems biology and bioinformatics in their work. It is also invaluable to several members of biomedical field who are interested in learning more about those approaches. - Encompasses one applicable example in every chapter to illustrate the solution procedure from big data mining, network modeling, host/pathogen cross-talk detection, drug target identification and systems drug design - Presents flowcharts to represent the development procedure of systematic immunology and infection in a very clear format - Contains 100 color diagrams to help readers understand the related biological networks, their corresponding mechanisms, and significant network biomarkers for therapeutic drug design
A much-needed guide through the overwhelming amount of literature in the field. Comprehensive and detailed, this book combines background information with the most recentinsights. It introduces current concepts, emphasizing the transcriptional control of genetic information. Moreover, it links data on the structure of regulatory proteins with basic cellular processes. Both advanced students and experts will find answers to such intriguing questions as: - How are programs of specific gene repertoires activated and controlled? - Which genes drive and control morphogenesis? - Which genes govern tissue-specific tasks? - How do hormones control gene expression in coordinating the activities of different tissues? An abundant number of clearly presented glossary terms facilitates understanding of the biological background. Speacial feature: over 2200 (!) literature references.
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.
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
This volume has a strong focus on homo-oligomerization, which is surprisingly common. However, protein function is so often linked to both homo- and hetero-oligomerization and many heterologous interactions likely evolved from homologous interaction, so this volume also covers many aspects of hetero-oligomerization.
Bacteria have been the dominant forms of life on Earth for the past 3.5 billion years. They rapidly evolve, constantly changing their genetic architecture through horizontal DNA transfer and other mechanisms. Consequently, it can be difficult to define individual species and determine how they are related. Written and edited by experts in the field, this collection from Cold Spring Harbor Perspectives in Biology examines how bacteria and other microbes evolve, focusing on insights from genomics-based studies. Contributors discuss the origins of new microbial populations, the evolutionary and ecological mechanisms that keep species separate once they have diverged, and the challenges of constructing phylogenetic trees that accurately reflect their relationships. They describe the organization of microbial genomes, the various mutations that occur, including the birth of new genes de novo and by duplication, and how natural selection acts on those changes. The role of horizontal gene transfer as a strong driver of microbial evolution is emphasized throughout. The authors also explore the geologic evidence for early microbial evolution and describe the use of microbial evolution experiments to examine phenomena like natural selection. This volume will thus be essential reading for all microbial ecologists, population geneticists, and evolutionary biologists.
