A Synthetic IL 10 Immune Logic Circuit: Hybrid Boolean–Stochastic Modeling, In Silico Validation on Human Transcriptomes, and Roadmap to Wet Lab Translation

We present the Adaptive Bio-Computational Control (ABC) framework: a programmable IL-10 logic circuit that senses pro-inflammatory cues and adaptively drives anti-inflammatory output. We validate the ABC Boolean gate on a large human transcriptomic cohort (GSE100833), map representative expression profiles into SSA-compatible initial conditions, and simulate time-resolved stochastic dynamics with Gillespie SSA across 100 trajectories and 100 virtual patients. Comparative in-silico experiments (TNF-blocker analog, IFNG knockout, TNF overexpression) show that ABC achieves superior inflammatory control (≈48% reduction in inflammatory burden vs ≈32% for TNF blockade), faster activation (median activation time t0 ≈ 8 units vs 10 units), and increased IL-10 output (≈30%–85% higher AUC depending on scenario). We provide detailed methods, quantitative results, and a translational roadmap outlining wet-lab validation (AAV delivery, circuit design in immune cells), biosafety/regulatory considerations, and experimental endpoints. This work situates hybrid Boolean–stochastic modeling as a robust preclinical pipeline for programmable immunomodulators.