Computational modeling of the IL-4 pathway to understand principles of systemic redox regulation in cell signaling
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Elevated levels of reactive oxygen species (ROS) cause or aggravate a variety of pathological conditions such as cardiovascular disease, cancer and rheumatoid arthritis. Despite known links between oxidative stress and disease, years of clinical studies have failed to show clear benefits of antioxidant therapy. It is now recognized that ROS such as hydrogen peroxide can act as signaling molecules and are required for physiological functioning of a number of signaling pathways. Therefore, a mechanistic basis of ROS-mediated regulation of cell signaling must be established to enable rational design of antioxidant-based therapies. The challenges in quantification of transient changes mediated by ROS during cell signaling have impeded investigation of redox-regulated signaling. In the present work, computational modeling is used to circumvent these technical challenges and to test competing hypotheses of redox regulation. Using a quantitative, systems level approach to study interactions between ROS dependent and independent regulatory mechanisms, the most comprehensive model of the IL-4 signaling pathway to date has been developed and validated with experimental data. The model is capable of predicting kinase phosphorylation dynamics under new oxidative conditions, and our analyses suggest that reversible oxidation of tyrosine phosphatases is the primary mechanism of redox regulation in this pathway. Additional computational methods have been developed to study ROS as mediators of crosstalk between signaling pathways, to optimize model parameters, and to interrogate model dynamics for the purpose of model selection. Collectively, these modeling tools provide a new systems-level perspective for investigating reversible protein oxidation as a means of control over cellular signal transduction.