Quantifying Molecular Forces with Serially Connected Force Sensors
Bibliographic record
Abstract
Abstract To understand the mechanical forces involved in cell adhesion, molecular force sensors have been developed to study tension through adhesion proteins. Recently, a class of molecular force sensors called tension gauge tether (TGT) have been developed that rely on irreversible force-dependent dissociation of DNA duplex to study cell adhesion forces. While the TGT offer high signal-to-noise ratio and is ideal for studying fast / single molecular adhesion processes, quantitative interpretation of experimental results has been challenging. Here we used computational approach to investigate how TGT fluorescence readout can be quantitatively interpreted. In particular we studied force sensors made of a single TGT, multiplexed single TGTs, and two TGTs connected in series. Our results showed that fluorescence readout using a single TGT can result from drastically different combinations of force history and adhesion event density that span orders of magnitude. In addition, the apparent behaviour of the TGT is influenced by the tethered receptor-ligand, making it necessary to calibrate the TGT with every new receptor-ligand. To solve this problem, we proposed a system of two serially connected TGTs. Our result shows that not only is the ratiometric readout of serial TGT independent of the choice of receptor-ligand, it is able to reconstruct force history with sub-pN force resolution. This is also not possible by simply multiplexing different types of TGTs together. Lastly, we systematically investigated how sequence composition of the two serially connected TGTs can be tuned to achieve different dynamic range. This computational study demonstrated how serially connected irreversible molecular dissociation processes can accurately quantify molecular force, and laid the foundation for subsequent experimental studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".