Single Cell Analysis In Biotechnology And Systems Biology

This book is a printed edition of the Special Issue "Single Cell Analysis in Biotechnology and Systems Biology" that was published in IJMS

Annotation Cells play significant roles in our day to day life. However, the interactions of cells, the cellular responses of organelles to molecules, and their intracellular behaviour, are still not fully understood. To better understand the physiological interactions among molecules, organelles, and cells, the ensemble measurement of (on average, millions of) cells cannot provide detailed information. However, for example, research concerning the differentiation behaviors of stem cells or the metastatic processes of tumour initiation requires detailed information. Understanding genomic sequence information at a single cell level can promote an understanding of how individual parts of a cell are integrated in time and space to form dynamic cellular processes. The relationship between cellular heterogeneity and signaling pathway regulation may result in an understanding of disease states that can potentially drive therapeutic interventions. Thus single cell analysis (SCA) has been emerging as a powerful method of investigating exciting new insights into genomics, fluxomics, proteomics, and systems biology.

This book provides an overview of single-cell isolation, separation, injection, lysis and dynamics analysis as well as a study of their heterogeneity using different miniaturized devices. As an important part of single-cell analysis, different techniques including electroporation, microinjection, optical trapping, optoporation, rapid electrokinetic patterning and optoelectronic tweezers are described in detail. It presents different fluidic systems (e.g. continuous micro/nano-fluidic devices, microfluidic cytometry) and their integration with sensor technology, optical and hydrodynamic stretchers etc., and demonstrates the applications of single-cell analysis in systems biology, proteomics, genomics, epigenomics, cancer transcriptomics, metabolomics, biomedicine and drug delivery systems. It also discusses the future challenges for single-cell analysis, including the advantages and limitations. This book is enjoyable reading material while at the same time providing essential information to scientists in academia and professionals in industry working on different aspects of single-cell analysis. Dr. Fan-Gang Tseng is a Distinguished Professor of Engineering and System Science at the National Tsing Hua University, Taiwan. Dr. Tuhin Subhra Santra is a Research Associate at the California Nano Systems Institute, University of California at Los Angeles, USA.

The rapidly developing field of systems biology is influencing many aspects of biological research and is expected to transform biomedicine. Some emerging offshoots and specialized branches in systems biology are receiving particular attention and are becoming highly active areas of research. This collection of invited reviews describes some of the latest cutting-edge experimental and computational advances in these emerging sub-fields of systems biology. In particular, this collection focuses on the study of mammalian embryonic stem cells; new technologies involving mass-spectrometry proteomics; single cell measurements; methods for modeling complex stochastic systems; network-based classification algorithms; and the revolutionary emerging field of systems pharmacology.

This volume addresses the latest state-of-the-art systems biology-oriented approaches that--driven by big data and bioinformatics--are utilized by Computational Systems Biology, an interdisciplinary field that bridges experimental tools with computational tools to tackle complex questions at the frontiers of knowledge in medicine and biotechnology. The chapters in this book are organized into six parts: systems biology of the genome, epigenome, and redox proteome; metabolic networks; aging and longevity; systems biology of diseases; spatiotemporal patterns of rhythms, morphogenesis, and complex dynamics; and genome scale metabolic modeling in biotechnology. In every chapter, readers will find varied methodological approaches applied at different levels, from molecular, cellular, organ to organisms, genome to phenome, and health and disease. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics; criteria utilized for applying specific methodologies; lists of the necessary materials, reagents, software, databases, algorithms, mathematical models, and dedicated analytical procedures; step-by-step, readily reproducible laboratory, bioinformatics, and computational protocols all delivered in didactic and clear style and abundantly illustrated with express case studies and tutorials; and tips on troubleshooting and advice for achieving reproducibility while avoiding mistakes and misinterpretations. The overarching goal driving this volume is to excite the expert and stimulate the newcomer to the field of Computational Systems Biology. Cutting-edge and authoritative, Computational Systems Biology in Medicine and Biotechnology: Methods and Protocols is a valuable resource for pre- and post-graduate students in medicine and biotechnology, and in diverse areas ranging from microbiology to cellular and organismal biology, as well as computational and experimental biologists, and researchers interested in utilizing comprehensive systems biology oriented methods.

Comprehensive coverage of the many different aspects of systems biology, resulting in an excellent overview of the experimental and computational approaches currently in use to study biological systems. Each chapter represents a valuable introduction to one specific branch of systems biology, while also including the current state of the art and pointers to future directions. Following different methods for the integrative analysis of omics data, the book goes on to describe techniques that allow for the direct quantification of carbon fluxes in large metabolic networks, including the use of 13C labelled substrates and genome-scale metabolic models. The latter is explained on the basis of the model organism Escherichia coli as well as the human metabolism. Subsequently, the authors deal with the application of such techniques to human health and cell factory engineering, with a focus on recent progress in building genome-scale models and regulatory networks. They highlight the importance of such information for specific biological processes, including the ageing of cells, the immune system and organogenesis. The book concludes with a summary of recent advances in genome editing, which have allowed for precise genetic modifications, even with the dynamic control of gene expression. This is part of the Advances Biotechnology series, covering all pertinent aspects of the field with each volume prepared by eminent scientists who are experts on the topic in question.

Systems biology is the study of organisms as interacting networks of genes, proteins and reactions. Practical Systems Biology provides a detailed overview of the different approaches used in this relatively new discipline, integrating bioinformatics, genomics, proteomics and metabolomics. Various areas of research are also discussed, including the use of computational models of biological processes, and post-genomic research. Each chapter is written by an experienced researcher and gives an excellent account of various issues of systems biology that is suitable for postgraduate and postdoctoral researchers who are interested in this expanding area of science.

Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.