A Framework for Generative AI-Designed Synthetic Gene Circuits for Targeted In Vivo Senolytic Therapy

Cellular senescence kind a contributes to age
related tissue dysfunction , but the senolytics right now they don’t really have that context specificity, and also safety can be kind of shaky. Here we show a computational framework for designing and screening senescence responsive synthetic gene circuits, using generative AI plus a risk aware evaluation layer. The whole workflow mixes together several bits, like: (i) a tissue aware senescence expression atlas built from harmonized single cell RNA‑seq datasets ; (ii) a senescence state classifier trained on atlas derived features, with explicit train/validation/test splits, (iii) a constraint aware conditional sequence generator aimed at regulatory DNA, (iv) a logic compiler that turns Boolean specs (AND/NOT) into genetic parts, (v) biophysical simulation of circuit dynamics through chemical reaction network ODEs, and (vi) a safety scoring module that tries to penalize OFF state leakiness, off target activation, and cellular burden. For candidate selection, we also lean on explainability tools—motif enrichment and SHAP—plus distribution free uncertainty quantification using conformal prediction, so risk sensitive picks are not just based on point estimates. We report dataset identifiers, model architectures, training hyperparameters, baseline comparisons, and ablation studies, and we include confidence intervals. All figures are numbered, referenced in text, and paired with statistical summaries. This study is explicitly computational, so no wet lab work or in vivo validation is claimed. Instead, we provide an auditable reproducible design protocol and a ranked set of candidate circuits meant for future pre-clinical evaluation. Overall, the work clarifies what methodological requirements and evaluation standards should look like for generative AI assisted senolytic circuit design.