An Introduction to Rule-based Modeling of Immune Receptor Signaling

TL;DR

This paper introduces rule-based modeling as an effective approach to simulate complex immune receptor signaling pathways, demonstrating its application to the FcεRI receptor system using the BioNetGen framework.

Contribution

It presents a comprehensive overview of rule-based modeling for immune signaling and provides a practical example with BioNetGen to address molecular complexity.

Findings

Rule-based modeling captures complex receptor interactions.

Application to FcεRI receptor demonstrates modeling effectiveness.

Open source BioNetGen software facilitates modeling efforts.

Abstract

Cells process external and internal signals through chemical interactions. Cells that constitute the immune system (e.g., antigen presenting cell, T-cell, B-cell, mast cell) can have different functions (e.g., adaptive memory, inflammatory response) depending on the type and number of receptor molecules on the cell surface and the specific intracellular signaling pathways activated by those receptors. Explicitly modeling and simulating kinetic interactions between molecules allows us to pose questions about the dynamics of a signaling network under various conditions. However, the application of chemical kinetics to biochemical signaling systems has been limited by the complexity of the systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune, PySB) is an approach to address this complexity. In this chapter, by application to the Fc$\varepsilon$RI receptor system, we will explore the origins of complexity in macromolecular interactions, show how rule-based modeling can be used to address complexity, and demonstrate how to build a model in the BioNetGen framework. Open source BioNetGen software and documentation are available at http://bionetgen.org.