Regulation of Immune Cell Fate by Intracellular Signaling Pathways: A Transparent Computational Framework for Disease and Therapy Hypothesis Generation

Abstract

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.