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Immuno Systems Biology aims to study the immune system in the more
integrated manner on how cells and molecules participate at
different system levels to the immune function. Through this book
Kumar Selvarajoo introduces to physicists, chemists, computer
scientists, biologists and immunologists the idea of an integrated
approach to the understanding of mammalian immune system. Geared
towards a researcher with limited immunological and computational
analytical experience, the book provides a broad overview to the
subject and some instruction in basic computational, theoretical
and experimental approaches. The book links complex immunological
processes with computational analysis and emphasizes the importance
of immunology to the mammalian system.
"Immuno Systems Biology" aims to study the immune system in the
more integrated manner on how cells and molecules participate at
different system levels to the immune function. Through this
bookKumar Selvarajoointroduces to physicists, chemists, computer
scientists, biologists and immunologists the idea of an integrated
approach to the understanding of mammalian immune system. Geared
towards a researcher with limited immunological and computational
analytical experience, the book provides a broad overview to the
subject and some instruction in basic computational, theoretical
and experimental approaches. The book links complex immunological
processes with computational analysis and emphasizes the importance
of immunology to themammalian system. "
This volume provides protocols for computational, statistical, and
machine learning methods that are mainly applied to the study of
metabolic engineering, synthetic biology, and disease applications.
These techniques support the latest progress in cross-disciplinary
research that integrates the different scales of biological
complexity. The topics covered in this book are geared toward
researchers with a background in engineering, computational
analytical, and modeling experience and cover a broad range of
topics in computational and machine learning approaches. 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. Comprehensive and practical, Computational
Biology and Machine Learning for Metabolic Engineering and
Synthetic Biology is a valuable resource for any researcher or
scientist who wants to learn more about the latest computational
methods and how they are applied toward the understanding and
prediction of complex biology.
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