463229 Mechanistic Model of CD3ζ Immunoreceptor Tyrosine-Based Activation Motif (ITAM) Phosphorylation Sequence
We constructed the model using BioNetGen, a rule-based formalism that allows us to account for the many species that arise from the multiple phosphorylation sites on CD3ζ. The model is implemented as a set of ordinary differential equations in MATLAB and fit to experimental data generated using an in vitro reconstituted membrane system that mimics the two-dimensional interactions that occur in T cells . We use mass spectrometry to measure the level of phosphorylation at each individual tyrosine residue in the system over time. To determine the order in which each of the six tyrosine residues on CD3ζ are phosphorylated, we first stimulate CD3ζ with a constitutively active form of LCK and fit our mechanistic model to this data. We then combine this model with a previously developed model of LCK autoregulation . The combined model will be validated using experimental data of wild-type LCK autophosphorylation and phosphorylation of CD3ζ. As a result, we generate a model that matches experimental data and can predict data not used in the fitting.
The model predicts the order and rate at which different ITAMs on the CD3ζ chain are phosphorylated, helping to decifer the kinetic proofreading mechanism that is thought to precisely regulate T cell activation. Additionally, we have applied the model to investigate how changes to the CD3ζ chain, such as removing the third ITAM, can affect overall activation. The results predicted by the model can be implemented in CD3ζ-bearing CAR-engineered T cells. Thus, the model generates new hypotheses that can be tested experimentally, allowing us to quantitatively explore T cell signaling and guide the development of immunotherapies. The model is a quantitative framework that can be used to examine the dynamics of CD3ζ chain activation by LCK and its effects on T cell activation.
 Chae, W., et al. Int. Immunol., 2004, 16(9), 1225-36.
 Hui, E. and R. Vale. Nat. Struct. Mol. Biol., 2014, 21(2), 133-142.
 Rohrs, J. A., Wang, P., and S. D. Finley. Cell Mol. Bioeng., 2016, In press.