Tuberculosis (TB) is an ancient and deadly disease characterized by
complex host-pathogen dynamics playing out over multiple time and length scales
and physiological compartments. Computational modeling can be used to integrate
various types of experimental data and suggest new hypotheses, mechanisms, and
therapeutic approaches to TB. Here, we offer a first-time comprehensive review
of work on within-host TB models that describe the immune response of the host
to infection, including the formation of lung granulomas. The models include
systems of ordinary and partial differential equations and agent-based models as
well as hybrid and multi-scale models that are combinations of these. Many
aspects of M. tuberculosis infection, including host dynamics
in the lung (typical site of infection for TB), granuloma formation, roles of
cytokine and chemokine dynamics, and bacterial nutrient availability have been
explored. Finally, we survey applications of these within-host models to TB
therapy and prevention and suggest future directions to impact this global
disease.