Monte Carlo Simulation of the Assembly of bis‐Biotinylated DNA and Streptavidin
Bibliographic record
Abstract
We present Monte Carlo simulations of the self-assembly of bivalent bis-biotinylated DNA molecules with the tetravalent biotin-binding protein streptavidin (STV). By fitting the STV binding probabilities for the four possible valencies, the modelling correctly reproduces the dependencies of various network parameters experimentally observed in an earlier study. The combined results from the experimental and theoretical studies suggest that the binding probability for divalent STV formation is about 50 times larger than for the formation of trivalent and about 200 times larger than for tetravalent STV. In accordance with the experimental results, the modelling also indicates that the mixture of an equimolar ratio of DNA and STV leads to a maximum in size of the oligomeric DNA-STV clusters formed. Furthermore, we found a percolation transition in which the DNA cluster size increases rapidly with increasing DNA concentration resulting in the formation of a single supercluster at elevated concentrations. This behaviour coincides with the occurrence of an immobile band previously observed in electrophoretic experiments, indicating the formation of extremely large DNA-STV aggregate networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".