In this volume, a wide-ranging series of reviews reveal how systems biology -- a holistic and inter-disciplinary approach requiring the combined talents of biologists, mathematicians, and computer scientists -- is changing the face of infectious disease research. Leading experts discuss how the use of high-throughput and computational approaches are generating exciting -- and often unexpected -- new insights into the microbial-host interactions of a variety of bacterial and viral pathogens, including Salmonella, Yersinia, Mycobacterium, influenza virus, human and simian immunodeficiency virus, and hepatitis C virus. Additional chapters focus on systems approaches to innate immunity, intra- and inter- cellular signaling, biomarker discovery, and the evaluation and rational development of improved vaccines. Systems biology has both been hailed as a paradigm shift that will revolutionize biological science and criticized as overly expensive and complex. While the truth no doubt lies somewhere in between, the approach is yielding increasingly detailed and comprehensive views of biological systems and processes, including those that dictate the host response to infection and disease outcome. Systems Biology of Infectious Disease is highly informative reading for investigators already engaged in systems biology research as well as for those scientists and clinicians who may be seeking an introduction to the field.

First, systems biology is an inter-disciplinary approach, requiring the combined talents of biologists, mathematicians, and computer scientists. Second, systems biology is holistic, with the goal of obtaining a comprehensive understanding of the workings of biological systems. This is achieved through the acquisition of massive amounts of data by high-throughput technologies-oligonucleotide microarrays, mass spectrometry, and next-generation sequencing-and the analysis of this data through sophisticated mathematical algorithms. It is perhaps the use of mathematics, to integrate abundant and diverse types of data and to generate models of interconnected molecular networks, that best characterizes systems biology.