A First Course in Systems Biology, Third Edition is an introduction to the growing field of systems biology for advanced undergraduates and graduate students. Its focus is the design and analysis of computational models and their applications to diverse biomedical phenomena, from simple networks and kinetics to complex pathway systems, signal transduction, personalized medicine, and interacting populations. The book begins with the fundamentals of computational modeling, then reviews features of the molecular inventories that bring biological systems to life and ends with case studies that reflect some of the frontiers in systems biology. In this way, the First Course provides the reader with a comprehensive background and with access to methods for executing standard tasks of biomedical systems analysis, exposure to the modern literature, and a foundation for launching into specialized projects that address biomedical questions with theoretical and computational means.

This third edition has been thoroughly updated. It provides an introduction to agent-based and multiscale modeling, a deeper account of biological design principles, and the optimization of metabolic flux distributions. This edition also discusses novel topics of synthetic biology, personalized medicine, and virtual clinical trials that are just emerging on the horizon of this field.

Eberhard O. Voit has been working in systems biology for over forty years. He is considered an expert on pathway analysis and biochemical systems theory. He is particularly interested in the education of the next generation of computational systems biologists and has numerous books and over 300 articles and book chapters to his credit.

Melissa L. Kemp is a systems biologist who has been combining experimental and computational studies for over two decades. She is interested in developing methods of computational systems biology to investigate the role of cellular metabolism in influencing information processing and cell fate decisions and modes of communication that drive self-organization in multicellular systems.