Regulation of Immune Cell Fate by Intracellular Signaling Pathways: A Transparent Computational Framework for Disease and Therapy Hypothesis Generation - Lalita, (The Bioscan, 2026)
Immune cell fate is shaped by the integration of antigenic, cytokine, metabolic, inhibitory and danger- associated signals. Many relevant intracellular pathways are individually well characterized, but their combined influence on immune-fate choice remains difficult to compare across disease settings. We developed a transparent literature-constrained computational framework that maps normalized pathway activity to five generalized immune-fate classes: effector activation, inflammatory myeloid polarization, regulatory tolerance, memory/stem-like persistence and exhaustion/apoptosis. The model combines signed pathway-to-fate coefficients, predefined pairwise crosstalk terms and softmax normalization. To improve reproducibility, this study reports the complete coefficient matrix, intercepts, crosstalk terms, disease-context vectors, therapeutic perturbation vectors, softmax temperature, random seeds and intervention scoring weights. Disease-context simulations predicted elevated effector and inflammatory probabilities in acute infection and autoimmune inflammation. Tumor immune suppression increased regulatory tolerance and exhaustion/apoptosis, while chronic infection was dominated by exhaustion/apoptosis pressure. Global sensitivity analysis across 12,000 simulated pathway-activity profiles identified checkpoint- SHP2/TOX as the strongest exhaustion-associated module, TGF-beta-SMAD as the main regulatory module, Wnt-beta-catenin and AMPK as memory-supporting modules and NF-kB/MAPK/cGAS- STING-IRF as inflammatory-polarization drivers. Therapeutic perturbation analysis suggested context dependence: NF-kB or JAK attenuation ranked highest in autoimmune inflammation, whereas checkpoint relief, combined mTOR/checkpoint modulation and TGF-beta blockade ranked highest in tumor immune suppression or chronic infection. These outputs are model-generated hypotheses rather than clinical recommendations. The framework is intended to prioritize experimentally testable pathway combinations before empirical calibration and validation.