The volume focuses on the genomics, proteomics, metabolomics, and bioinformatics of a single cell, especially lymphocytes and on understanding the molecular mechanisms of systems immunology. Based on the author’s personal experience, it provides revealing insights into the potential applications, significance, workflow, comparison, future perspectives and challenges of single-cell sequencing for identifying and developing disease-specific biomarkers in order to understand the biological function, activation and dysfunction of single cells and lymphocytes and to explore their functional roles and responses to therapies. It also provides detailed information on individual subgroups of lymphocytes, including cell characters, function, surface markers, receptor function, intracellular signals and pathways, production of inflammatory mediators, nuclear receptors and factors, omics, sequencing, disease-specific biomarkers, bioinformatics, networks and dynamic networks, their role in disease and future prospects. Dr. Xiangdong Wang is a Professor of Medicine, Director of Shanghai Institute of Clinical Bioinformatics, Director of Fudan University Center for Clinical Bioinformatics, Director of the Biomedical Research Center of Zhongshan Hospital, Deputy Director of Shanghai Respiratory Research Institute, Shanghai, China.

"Taken together, the body of information contained in this book provides readers with a bird’s-eye view of different aspects of exciting work at the convergence of disciplines that will ultimately lead to a future where we understand how immunity is regulated, and how we can harness this knowledge toward practical ends that reduce human suffering. I commend the editors for putting this volume together." –Arup K. Chakraborty, Robert T. Haslam Professor of Chemical Engineering, and Professor of Physics, Chemistry, and Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA New experimental techniques in immunology have produced large and complex data sets that require quantitative modeling for analysis. This book provides a complete overview of computational immunology, from basic concepts to mathematical modeling at the single molecule, cellular, organism, and population levels. It showcases modern mechanistic models and their use in making predictions, designing experiments, and elucidating underlying biochemical processes. It begins with an introduction to data analysis, approximations, and assumptions used in model building. Core chapters address models and methods for studying immune responses, with fundamental concepts clearly defined. Readers from immunology, quantitative biology, and applied physics will benefit from the following: Fundamental principles of computational immunology and modern quantitative methods for studying immune response at the single molecule, cellular, organism, and population levels. An overview of basic concepts in modeling and data analysis. Coverage of topics where mechanistic modeling has contributed substantially to current understanding. Discussion of genetic diversity of the immune system, cell signaling in the immune system, immune response at the cell population scale, and ecology of host-pathogen interactions.

The book focuses on various detection targets applied in single cell studies, including tumor tissue cells, circulating tumor cells (CTCs), disseminated tumor cells (DTCs), circulating tumor DNA (ctDNA), cell-free DNA (cfDNA) and cancer stem cells (CSCs). It also discusses and compares detection methods using these detection targets in different fields to reveal single cell biomedical functions. The volume focuses not only on the methods already been established and validated, and also the methods newly developed. The book also highlights the importance and potential of single cell biomedicine in the development and validation of precision medicine strategies. It is useful for researchers and students in the field of cell biology, molecular medicine and precision medicine etc.

With the rapid development of biotechnologies, single-cell sequencing has become an important tool for understanding the molecular mechanisms of diseases, defining cellular heterogeneities and characteristics, and identifying intercellular communications and single-cell-based biomarkers. Providing a clear overview of the clinical applications, the book presents state-of-the-art information on immune cell function, cancer progression, infection, and inflammation gained from single-cell DNA or RNA sequencing. Furthermore, it explores the role of target gene methylation in the pathogenesis of diseases, with a focus on respiratory cancer, infection and chronic diseases. As such it is a valuable resource for clinical researchers and physicians, allowing them to refresh their knowledge and improve early diagnosis and therapy for patients.

Recent technological advances in single-cell microbiology, using flow cytometry, microfluidics, x-ray fluorescence microprobes, and single-cell -omics, allow for the observation of individuals within populations. Simultaneously, individual-based models (or more generally agent-based models) allow for individual microbes to be simulated. Bridging these techniques forms the foundation of individual-based ecology of microbes (µIBE). µIBE has elucidated genetic and phenotypic heterogeneity that has important consequences for a number of human interests, including antibiotic or biocide resistance, the productivity and stability of industrial fermentations, the efficacy of food preservatives, and the potential of pathogens to cause disease. Individual-based models can help us to understand how these sets of traits of individual microbes influence the above. This eBook compiles all publications from a recent Research Topic in Frontiers in Microbiology. It features recent research where individual observational and/or modelling techniques are applied to gain unique insights into the ecology of microorganisms. The Research Topic “The Individual Microbe: Single-Cell Analysis and Agent-Based Modelling” arose from the 2016 @ASM conference of the same name hosted by the American Society for Microbiology at its headquarters in Washington, D.C. We are grateful to ASM for funding and hosting this conference.

Proteomics and Systems Biology, Volume 127 in the Advances in Protein Chemistry and Structural Biology series, outlines current proteomic methodologies and discuss the challenges in future applications of systems biology in a number of biomedical/bioscience subjects. In last few decades, advances in genomics, proteomics, metabolomics, glycomics, venomics, etc., have produced vast large-scale datasets that need to be analyzed with a single main objective of understanding biological systems as a whole. Such understanding will allow us to predict and characterize the dynamic properties of biological systems. - Integrates experimental and computational methods for understanding biological systems as a whole - Contains timely chapters written by well-renowned authorities in their field - Includes well supported content that is accompanied by a number of high-quality illustrations, figures and tables, hence it targets a wide audience of specialists, researchers and students

Due to the complexity and interconnectivity of many biological processes, an integrative systems biology approach is required to better understand them. The integration of various omics data (including genomics, proteomics, transcriptomics, and metabolomics data) will provide a holistic view that highlights the relationship among the various biomolecules and their functions in plant growth, resistance, quality, and many other important traits. Recent advances in analytical power and in the ability to process large data sets has enabled researchers to integrate multi-omics data and have a much deeper knowledge of the cell, tissue, organ, or even an entire organism being studied. The goal of this Research Topic is not only to publish high-quality and reliable omics data but also to answer the bigger question of how to integrate and correlate different data sets from multiple omics studies. Can one omics data set be used as a primary data set for the design and execution of another omics study? If so, then how it can be done? What are its pre-requisites? How can we plan research involving multi-omics approach to reach a meaningful conclusion? These are the questions that need our attention and their answers are critical for the future of omics-based research. For this Research Topic, we welcome contributors to submit manuscripts related to the following themes: • Studies on plant-based genomics, transcriptomics, proteomics, and metabolomics; • Development of new analytical methods for omics utilizing in plants; • Advancements in the current analytical methods for utilizing omics in plants; • Integration of multi-omics data sets; • Development of new statistical methods for data analysis.

System Biology Approaches for Microbial Pathogenesis Interaction Analysis aids biological researchers to expand their research scope using piled up data generated through recent technological advancement. In addition, it also opens avenues for bioinformatics and computer science researchers to utilize their expertise in biological meaningful ways. It also covers network biology approaches to decipher complex multiple host-pathogen interactions in addition to giving valuable coverage of artificial intelligence. The host-pathogen interactions are generally considered as highly specific interactions leading to a variety of consequences. The utilization of data science approaches has revolutionized scientific research including host-pathogen interaction analyses. Data science approaches coupled with network biology has taken host-pathogen interaction analysis from specific interaction to a new paradigm of understanding consequences of these interaction in the biological network. Unfortunately, basic biological researchers are mostly unaware of these advancements. In contrast, data scientists are not familiar with biological aspects of such data. System Biology Approaches for Microbial Pathogenesis Interaction Analysis will bridge these gaps through a new paradigm of understanding consequences of interaction in biological networks. • Cover approaches to decipher complex multiple host–pathogen interactions• Gives biological researcher an insight into the utilization of technological advancements in the field of host–pathogen interaction analyses in their work• Provides a new paradigm of understanding the consequences of host–pathogen interaction in biological systems