A logical model provides insights into T cell receptor signaling

• Published in: PLoS computational biology 2007-08, Vol.3 (8), p.e163-e163
• Main Authors: Saez-Rodriguez, Julio, Simeoni, Luca, Lindquist, Jonathan A, Hemenway, Rebecca, Bommhardt, Ursula, Arndt, Boerge, Haus, Utz-Uwe, Weismantel, Robert, Gilles, Ernst D, Klamt, Steffen, Schraven, Burkhart
• Format: Article
• Language: English
• Subjects: Antigen receptors, T cell; Autoimmune diseases; Biological models; Computational Biology; Computer Simulation; Experiments; Graphs; Immunology; Kinases; Logistic Models; Lymphocytes; Mammals; Models, Immunological; Receptors; Receptors, Antigen, T-Cell - immunology; Signal Transduction - immunology; T cell receptors; T cells; T-Lymphocytes - immunology
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.0030163

Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.