Engineering Biosensors with Dual Programmable Dynamic Ranges
Bibliographic record
Abstract
Although extensively used in all fields of chemistry, molecular recognition still suffers from a significant limitation: host-guest binding displays a fixed, hyperbolic dose-response curve, which limits its usefulness in many applications. Here we take advantage of the high programmability of DNA chemistry and propose a universal strategy to engineer biorecognition-based sensors with dual programmable dynamic ranges. Using DNA aptamers as our model recognition element and electrochemistry as our readout signal, we first designed a dual signaling "signal-on" and "signal-off" adenosine triphosphate (ATP) sensor composed of a ferrocene-labeled ATP aptamer in complex to a complementary, electrode-bound, methylene-blue labeled DNA. Using this simple "dimeric" sensor, we show that we can easily (1) tune the dynamic range of this dual-signaling sensor through base mutations on the electrode-bound DNA, (2) extend the dynamic range of this sensor by 2 orders of magnitude by using a combination of electrode-bound strands with varying affinity for the aptamers, (3) create an ultrasensitive dual signaling sensor by employing a sequestration strategy in which a nonsignaling, high affinity "depletant" DNA aptamer is added to the sensor surface, and (4) engineer a sensor that simultaneously provides extended and ultrasensitive readouts. These strategies, applicable to a wide range of biosensors and chemical systems, should broaden the application of molecular recognition in various fields of chemistry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".