# Modular Control of Boolean Network Models

## TL;DR

This paper introduces a modular approach to control biological networks, using Boolean models to efficiently identify control strategies for complex systems like cancer.

## Contribution

A novel modular control framework for Boolean networks that leverages modularity and canalizing features to identify efficient control strategies.

## Key findings

- Modular decomposition enables efficient identification of control strategies in Boolean networks.
- Canalizing features help identify modules irrelevant to control, reducing computational complexity.
- The approach was successfully applied to a T-LGL leukemia model to find a minimal control set.

## Abstract

The concept of control is crucial for effectively understanding and applying biological network models. Key structural features relate to control functions through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications often focus on model-based control, such as in biomedicine or metabolic engineering. In a recent paper, the authors developed a theoretical framework of modularity in Boolean networks, which led to a canonical semidirect product decomposition of these systems. In this paper, we present an approach to model-based control that exploits this modular structure, as well as the canalizing features of the regulatory mechanisms. We show how to identify control strategies from the individual modules, and we present 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 challenging. Our modular approach leads to an efficient approach to solving this problem. We apply it 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.