Mathematical model of the rate‐limiting steps for retrovirus‐mediated gene transfer into mammalian cells
Bibliographic record
Abstract
A quantitative understanding of the process of retrovirus-mediated gene transfer into mammalian cells should assist the design and optimization of transduction protocols. We present a mathematical model of the process that incorporates the essential rate-limiting transduction steps including diffusion, convection and decay of viral vectors, their binding at the cell surface and entry into the cell cytoplasm, reverse transcription of uncoated RNA to form DNA intermediates, transport of the latter through the cytosol to the cell nucleus and, finally, nuclear import and integration of the delivered DNA into the target cell genome. Cell and virus population balances are used to account for the kinetics of multiple vector infections which influence the transduction efficiency and govern the integrated copy number. The mathematical model is validated using gibbon ape leukemia virus envelope pseudotyped retroviral vectors and K562 target cells. Viral intermediate complexes derived from the internalized retroviral vectors are found to remain stable inside the K562 cells and the cytoplasmic trafficking time is consistent with the time scale for retrovirus uncoating, reverse transcription and transport to the cell nucleus. The model predictions of transduction efficiency and integrated copy number agree well with experimental data for both static (i.e., standard gravity) and centrifugation-based gene transfer protocols. The formulation of the model can also be applied to transduction protocols involving lenti- or foamy-viruses and so should prove to be useful for the optimization of several types of gene transfer processes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".