Kinetically Programmed Signaling Cascades for Molecular Detection
Bibliographic record
Abstract
Upon external stimulation, living cells activate a series of precisely programmed signaling cascade reactions that efficiently convert multiple stimuli into relevant output signals, thereby maintaining physiological homeostasis. These naturally evolved molecular networks have recently inspired the development of new chemical strategies in fields ranging from synthetic biology to molecular computing, drug delivery, and biosensing systems. While some studies have begun to explore how programming the thermodynamics of these bioinspired chemical systems can optimize their performance, the impact of programming their kinetics remains largely unexplored. Here, we leveraged the modularity and programmability of DNA chemistry to develop a simple DNA-based signaling cascade to measure the concentration of specific molecules and investigated how programming its kinetics affects its performance. This signaling cascade comprises four modules (input, receptor, processor, and output) and three molecular interactions for which we have characterized all intrinsic rate constants. Through simulations and experiments, we demonstrated that careful kinetic programming can significantly enhance the rate, gain, and sensitivity of the signaling cascade output. We further illustrated the versatility and modularity of this cascade by adapting it for the detection of four different molecules ( small molecules and proteins). We also showed that it can be readily adapted into a rapid, one-step, inexpensive electrochemical sensor enabling therapeutic drug monitoring (TDM) at home directly from a drop of blood. We believe that similar kinetically programmed signaling cascades could be developed for a wide range of chemical applications, allowing complex, multistep workflows to be streamlined into rapid, single-step reactions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".