"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. "

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.

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.