  Interacting biological systems at all organizational levels display emergent
behavior. Modeling these systems is made challenging by the number and variety
of biological components and interactions (from molecules in gene regulatory
networks to species in ecological networks) and the often-incomplete state of
system knowledge (e.g., the unknown values of kinetic parameters for
biochemical reactions). Boolean networks have emerged as a powerful tool for
modeling these systems. We provide a methodological overview of Boolean network
models of biological systems. After a brief introduction, we describe the
process of building, analyzing, and validating a Boolean model. We then present
the use of the model to make predictions about the system's response to
perturbations and about how to control (or at least influence) its behavior. We
emphasize the interplay between structural and dynamical properties of Boolean
networks and illustrate them in three case studies from disparate levels of
biological organization.
