Efficient simulation of arbitrary multi-component first-order binding kinetics for improved assay design and molecular assembly
Bibliographic record
Abstract
Traditional ELISA, long the workhorse for specific target protein detection using microplate wells, is nearing its fundamen-tal limit of sensitivity. New opportunities in healthcare call for in vitro diagnostic tests with ultra-high sensitivity. Magnetic bead-based ELISA formats have been developed that can reach unprecedented sensitivities order of magnitude better than are allowed for by the rate constants for a single ligand-receptor interaction. However, these ultra-high sensitivity assays are highly vulnerable to a host of confounding factors, including nonspecific binding from background molecules and loss of low-abundance target to tube walls and during wash steps. Moreover, optimization of workflow is often time-consuming and expensive. In this work, we present a simulation tool that allows users to graphically define arbitrary binding assays, includ-ing fully reversible first-order binding kinetics, timed addition of extra components, and timed wash steps. The tool is freely available as a user-friendly webapp. The framework is lightweight and fast, allowing for inexpensive simulation and visuali-zation of arbitrarily complex assay schemes, including but not limited to digital immunoassays, DNA hybridization, and enzyme kinetics, for validation and optimization of assay designs without requiring any programming knowledge from the user. We demonstrate some of these capabilities and provide practical guidance on assay simulation design.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".