Demystifying the cytokine network: Mathematical models point the way

Abstract

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

Keywords: CD4 T cell differentiation; Cytokine networks; Mathematical modeling; Single-cell analysis.

Fig. 1

Modeling approaches used to study cytokine interactions. Two main modeling approaches are used; knowledge-based approaches that may utilize Boolean logic, ordinary differential equations (ODE) or rule-based techniques or data-driven approaches in which large experimental datasets are analyzed using principal component analysis (PCA), network or information theory. This is an iterative process with experimental data used to create and validate models, models analyzed to generate predictions which are then tested in further experiments.