Signal‑to‑Molecule (S2M): A Multi‑Cues Framework for Dust–Pollen–Ant–Microbiome Driven Biosynthetic Editing
Natural products often emerge when microbes sense specific, time‑ordered cues in their habitat. Existing methods activate latent biosynthetic gene clusters (BGCs) via single elicitors (e.g., γ‑butyrolactones) or Chem‑CRISPR gates that couple Cas activity to one molecule. Signal‑to‑Molecule (S2M) instead targets signal space: it extracts and replays ecological opcodes—multi‑channel environmental signatures such as pH↓ (formic/organic acids) → Fe/Si from mineral dust → fungal VOC (1‑octen‑3‑ol)—optionally including a brief UV/flash‑heat pulse, inside a programmed host (e.g., Streptomyces M1152). To preserve signal fidelity, field capture uses an active field‑to‑chip microfluidic sampler with on‑chip pH buffering, Fe‑chelation (DFOB) to maintain Fe³⁺ solubility, VOC traps (PDMS/Carbopack) for timed release, and a 4 °C cold‑chain; we define Signal‑Integrity KPIs (VOC recovery ≥80%, Fe solubility ≥90%, enzyme activity ≥50%). Reconstitution integrates pollen microgels and a dust‑assisted catalysis module (Fe‑catecholate MOF or Fe‑doped silica), plus real‑time VOC sensing (PTR‑MS/MOS/PID) under ISO/ASTM standards. Robustness and search efficiency come from time‑series ML and Bayesian/RL optimization with signal‑grammar constraints, while droplet microfluidics enables 10³ parallel replays. To avoid host deafness, S2M supports a wild co‑pilot (including cell‑free lysate mode) that translates complex cues into quorum‑like signals readable by the lab chassis. Rather than editing DNA, S2M routes metabolism across PKS/NRPS modules via programmable promoter logic responsive to pH/Fe/VOC. A three‑stage roadmap—(1) field sampling and multi‑omics, (2) feature selection + adaptive optimization to derive a replayable signature, (3) reconstitution with mineral co‑factors and optional co‑pilot—establishes signal‑edited metabolomes as a complementary path to CRISPR. We also outline an S2M‑Lite path (<$10K) for rapid POC.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-10-12
- DOI
- https://doi.org/10.5281/zenodo.17333848
- Primary Topic
- Diverse Scientific and Economic Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00