Statistical Model Checking in BioLab: Applications to the Automated Analysis of T-Cell Receptor Signaling Pathway

Like other cell-surface receptors with intrinsic or associated protein-tyrosine kinase activity, the T-cell receptor complex undergoes a number of modifications, including tyrosine phosphorylation steps, after ligand binding but before transmitting a signal. The requirement for these modifications introduces a temporal lag between ligand binding and receptor signaling. A model for the T-cell receptor is proposed in which this feature greatly enhances the receptor's ability to discriminate between a foreign antigen and self-antigens with only moderately lower affinity. The proposed scheme is a form of kinetic proofreading, known to be essential for the fidelity of protein and DNA synthesis. A variant of this scheme is also described in which a requirement for formation of large aggregates may lead to a further enhancement of the specificity of T-cell activation. Through these mechanisms, ligands of different affinity potentially may elicit qualitatively different signals.

BioNetGen allows a user to create a computational model that characterizes the dynamics of a signal transduction system, and that accounts comprehensively and precisely for specified enzymatic activities, potential post-translational modifications and interactions of the domains of signaling molecules. The output defines and parameterizes the network of molecular species that can arise during signaling and provides functions that relate model variables to experimental readouts of interest. Models that can be generated are relevant for rational drug discovery, analysis of proteomic data and mechanistic studies of signal transduction.