Analysis with Mathematical Models Provides Insights into Infectious Diseases

M uch of microbiological research on infectious agents is increasingly specialized and reductionist, viewing the host as a virtually static environment and thus sharply narrowing the context of the disease under study. We advocate an alternative quantitative approach that enables investigators to go beyond their reliance on conventional and somewhat simplified qualitative terms for describing experimental outcomes—typically, “gene x is required to control infection y.” While those conventional approaches give rise to detailed knowledge of components involved in pathogenesis and host immunity, they leave important questions unanswered. Here we consider three. (i) What are the processes such as cell death and divisions that underlie variations in bacterial numbers in sets of experiments involving infections in animals? (ii) What is the temporal organization of immune response during an infection? (iii) How do dynamic responses within an infected individual relate to the spread of pathogens between individuals and within populations? Examples of recent model-based approaches that address these three questions provide a representative sampling of what several research groups are doing to examine the responses of complex mammalian immune systems to infectious agents. These mathematical model-augmented studies build on the strengths of reductionist approaches with which molecular microbiologists are familiar. It is our hope that increasing numbers of microbiologists will recognize the value of these approaches and also take advantage of opportunities to work with mathematical modelers when examining system-level aspects of infectious diseases. Based on personal experience, such collaborations can be stimulating and fun, while providing important insights into infectious diseases.