Title: Introduction to Computational Systems Biology : Systems-level Modelling of Cellular Networks
Publisher: Chapman and Hall/CRC
Publication Date: 2021
Language: English
ISBN 10: 1138597325
ISBN 13: 9781138597327
Binding: Hardcover
Condition: As New

About this title

Synopsis

This book delivers a comprehensive and insightful account of applying mathematical modelling approaches to very large biological systems and networks—a fundamental aspect of computational systems biology. The book covers key modelling paradigms in detail, while at the same time retaining a simplicity that will appeal to those from less quantitative fields.
Key Features:

A hands-on approach to modelling
Covers a broad spectrum of modelling, from static networks to dynamic models and constraint-based models
Thoughtful exercises to test and enable understanding of concepts
State-of-the-art chapters on exciting new developments, like community modelling and biological circuit design
Emphasis on coding and software tools for systems biology
Companion website featuring lecture videos, figure slides, codes, supplementary exercises, further reading, and appendices: ramanlab.github.io/SysBioBook/

An Introduction to Computational Systems Biology: Systems-Level Modelling of Cellular Networks is highly multi-disciplinary and will appeal to biologists, engineers, computer scientists, mathematicians and others.

About the Author

Dr. Karthik Raman is an Associate Professor at the Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras. He co-founded and co-ordinates the Initiative for Biological Systems Engineering and is a core member of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI). He has been a researcher in the area of systems biology for the last 15+ years and has been teaching a course on systems biology for the last eight years, to (mostly) engineers from different backgrounds. His lab works on computational approaches to understand and manipulate biological networks, with applications in metabolic engineering and synthetic biology.

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