  The concept of control is central to understanding and applications of
biological network models. Some of their key structural features relate to
control functions, through gene regulation, signaling, or metabolic mechanisms,
and computational models need to encode these. Applications of models often
focus on model-based control, such as in biomedicine or metabolic engineering.
This paper presents an approach to model-based control that exploits two common
features of biological networks, namely their modular structure and canalizing
features of their regulatory mechanisms. The paper focuses on intracellular
regulatory networks, represented by Boolean network models. A main result of
this paper is that control strategies can be identified by focusing on one
module at a time. This paper also presents a criterion based on canalizing
features of the regulatory rules to identify modules that do not contribute to
network control and can be excluded. For even moderately sized networks,
finding global control inputs is computationally very challenging. The modular
approach presented here leads to a highly efficient approach to solving this
problem. This approach is applied to a published Boolean network model of blood
cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control
set that achieves a desired control objective.
