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Full Description
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.
Contents
Computational Systems Biology and Artificial Intelligence.- Bioinformatic Analysis of CircRNA from RNA-seq Datasets.- Single-Cell Analysis of the Transcriptome and Epigenome.- Automatic Assignment, Quantitation, and Biological Annotation of Redox Proteomics Datasets with ProteoSushi.- A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.- MITODYN: An Open Source Software for Quantitative Modeling of Mitochondrial and Cellular Energy Metabolic Flux Dynamics in Health and Disease.- Integrated Multi-Omics, Bioinformatics, and Computational Modeling Approaches to Central Metabolism in Organs.- Understanding the Human Aging Proteome using Epidemiological Models.- Unraveling Pathways of Health and Lifespan with Integrated Multi-Omics Approaches.- UT-Heart: A Finite Element Model Designed for the Multi-Scale and Multi-Physics Integration of Our Knowledge on the Human Heart.- Multiscale Modeling of the Mitochondrial Origin of Cardiac Reentrant and Fibrillatory Arrhythmias.- Automated Quantification and Network Analysis of Redox Dynamics in Neuronal Mitochondria.- Computational Approaches and Tools as Applied to the Study of Rhythms and Chaos in Biology.- Computational Systems Biology of Morphogenesis.- Agent-Based Modeling of Complex Molecular Systems.- Metabolic Modeling of Wine Fermentation at Genome Scales.- Modeling Approaches to Microbial Metabolism.
