Acidity-Responsive Therapeutic Gene Circuits for Tumor Microenvironment Reprogramming: A Computational Design Framework

Background : Tumor microenvironments (TMEs) are characterized by pathological acidity (pH ~6.5) that suppresses immune function, promotes metastasis, and reduces therapeutic efficacy. Synthetic gene circuits offer programmable responses to TME acidity, but rational design has been hindered by the lack of integrated computational frameworks that couple circuit dynamics with tumor biology and therapeutic outcomes. Methods: We developed a comprehensive computational framework for acidity-responsive therapeutic gene circuits that sense TME pH to express alkalinizing agents and immunostimulatory cytokines. The model integrates seven coupled processes: tumor growth kinetics, effector T-cell responses with acid-mediated suppression, engineered bacterial vector dynamics, pH-responsive promoter activation, alkalinizing enzyme production, cytokine secretion, and proton concentration dynamics calibrated to physiological pH ranges (6.0-7.4). We compared two alkalinization strategies—substrate-dependent urease systems versus substrate-independent bicarbonate production via carbonic anhydrase—through sensitivity analysis and parameter sweeps. The framework was extended to 2D reaction-diffusion models for spatial analysis and validated through numerical stability analysis. Results: Simulations predict that optimized acidity-responsive circuits achieve sustained pH normalization (ΔpH = 0.2-0.6 units) with concurrent 3-7-fold T-cell activation enhancement, leading to >90% tumor reduction within 30-35 hours. Sensitivity analysis identified four critical design parameters: neutralization strength, cytokine-driven T-cell expansion rate, promoter activation threshold, and immune clearance kinetics. Metabolic burden analysis revealed that urease-based alkalinization imposes minimal energetic costs at microscale (~1% ATP flux) but scales prohibitively with tumor volume, reaching 80-100% ATP allocation at clinically relevant dimensions (1 cm³). In contrast, bicarbonate systems via carbonic anhydrase impose <0.01% metabolic burden independent of scale, explaining their superior and consistent performance across all parameter regimes. Spatial modeling demonstrates confined therapeutic activity within acidic tumor cores (pH < 6.6) while maintaining specificity at tissue boundaries, with promoter threshold tuning providing quantitative control over the therapeutic window. Conclusions: This work establishes the first integrated computational framework coupling acidity-responsive bacterial gene circuits with tumor-immune dynamics for TME-targeted therapeutic design, providing quantitative design principles and mechanistic insights for synthetic biology cancer therapeutics. The identification of bicarbonate-based alkalinization as energetically superior to urease systems, coupled with validated design parameters for promoter tuning and dosing strategies, enables rational optimization of pH-responsive circuits. Our model-guided approach addresses critical gaps in predictive tools for synthetic biology therapeutics and provides a translational pathway toward engineered bacterial cancer therapeutics and pH-responsive cellular delivery platforms.