Most immunogenomic models predict T-cell receptor–peptide binding. KSHAMA inverts this: given a primary human CD4⁺ T cell and a candidate genetic perturbation (knockout, knockdown, or CRISPR activation), it predicts whether the perturbation drives commitment to a stable, suppressive FOXP3⁺ regulatory (Treg) fate, whether that state is durable, and it ranks perturbations for wet-lab prioritization. Anchored on the biology recognized by the 2025 Nobel Prize (peripheral tolerance, Tregs, FOXP3) and its lethal failure mode, IPEX, we (i) map 292 recent papers of the subfield, (ii) construct a signed, directed regulatory network of 48 circuit genes whose edges are 82% supported by STRING v12.0, (iii) identify the highest-leverage “trigger” nodes by network centrality, and (iv) simulate how single perturbations cascade toward stable Treg or effector/autoimmune fate using a continuous-logical dynamical model. The simulation reproduces the IPEX collapse (FOXP3 loss de-represses the T-bet effector axis) and recovers RBPJ—a repressor identified by a 2025 genome-wide CRISPR screen—as the top-ranked trigger. All mechanistic claims were audited by an independent reviewer agent against primary literature. We enumerate live datasets (genome-scale Perturb-seq in primary human CD4⁺ T cells), baseline models with hyperparameters, benchmarking harnesses, and four therapeutic modalities through which ranked perturbations become interventions.
Team
Placement
Participant
