Mathematical modeling and sensitivity analysis of synNotch-CAR T-cells identify engineering targets for dynamic tunability

bioRxiv

Alexander J. Diefes, Bashir Sbaiti, Maria-Veronica Ciocanel, Cameron M. Kim

a. Cubic ternary complex model schematic for synNotch. Each corner of the cube represents a theoretically-possible state for synNotch. L corresponds to ligand bound, * corresponds to an activated synNotch conformation, # corresponds to a cleaved state with an exposed PEST tag, and TF corresponds to transcription factor bound. The PEST tag is exposed once the transcription factor is cleaved and leaves the receptor (13), which signals degradation of the receptor (51). Each orthogonal axis represents a physical property: ligand binding, receptor activation, and transcription factor release. b. Primary (ligand-dependent) activation pathway of synNotch. Ligand (blue circle) binds to the extracellular domain of the synNotch receptor (purple curve), which induces activation of the receptor (yellow line). The transcription factor (pink square) is then cleaved and sent to the nucleus. The ligand then dissociates and the receptor eventually degrades. The binding, activation, and dissociation reactions are characterized by the rate constants kL, kact,L, and k−L, respectively; the release of transcription factor is treated as a quasi-steady-state approximation. c. Ligand-independent activation pathway of synNotch. The cartoon components are the same as those in the ligand-dependent activation pathway. The receptor activates in the absence of ligand at rate kact. d. Schematic of the relevant receptor states and kinetic pathways in the cubic TCM and gene expression model. The variables and parameters for the cubic TCM are given in Table 1, and the variables and parameters for the gene expression model are given in Table 2.

Summary

Cancer therapeutics are increasingly incorporating engineered receptors due to their ability to detect extracellular ligands and initiate intracellular responses that regulate gene expression. By redesigning these natural signaling systems, synthetic receptors hold great potential for use in novel cell-based therapies. One particularly promising direction is modifying the Notch receptor, a transmembrane protein that naturally mediates ligand-dependent signaling at the cell surface to regulate cell proliferation and differentiation in neurogenesis. Both the intracellular and extracellular domains of Notch can be replaced with alternative domains, creating the family of modified Notch receptors known as synthetic Notch (synNotch). In existing synNotch-activated chimeric antigen receptor (CAR) T-cells, the extracellular domain can be engineered to adjust binding affinity for a specific cancer antigen, enabling precise tuning of therapeutic activity while minimizing off-target effects. To quantify and inform such tuning, we develop differential equations models of synNotch receptor signaling and subsequent gene expression. The mathematical models couple activation dynamics on fast timescales (characteristic of receptor-ligand interactions) and on slow timescales (characteristic of downstream gene expression dynamics). Global Sobol sensitivity analysis of the proposed models highlights parameters that yield the greatest variability in synNotch signal transduction and gene expression, indicating their potential to be engineered for different functions in future cancer therapeutics. For the receptor-ligand interactions in the synNotch model, we find that ligand association and ligand-independent activation are the most sensitive parameters. In the downstream gene expression model, promoter strength and degradation rates of mRNA and gene product are found to be most amenable to engineering.

Citation

Diefes, Alexander J., et al. “Mathematical modeling and sensitivity analysis of synNotch-CAR T-cells identify engineering targets for dynamic tunability.” bioRxiv (2026): 2026-03.

BibTex

@article{diefes2026mathematical, title={Mathematical modeling and sensitivity analysis of synNotch-CAR T-cells identify engineering targets for dynamic tunability}, author={Diefes, Alexander J and Sbaiti, Bashir and Ciocanel, Maria-Veronica and Kim, Cameron M}, journal={bioRxiv}, pages={2026–03}, year={2026}, publisher={Cold Spring Harbor Laboratory} }

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