Designing in vitro tools to pattern gene expression using inducible gene expression
Bibliographic record
Abstract
Early embryogenesis involves the sequential patterning of gene expression in different regions of the developing embryo, with the ultimate purpose of guiding initially identical cells into different paths of differentiation and thus facilitating body part formation and patterning. Being able to reproduce patterns of gene expression that lead to formation of a specific tissue in vitro would be useful both for engineering artificial tissues, and for developing simplified in vitro models to study and define the rules that guide tissue morphogenesis. Here we report a system to pattern gene expression in epithelial sheets using a drug inducible gene expression system. We created a sheet of epithelial cells transduced with a doxycycline inducible lentivirus encoding GFP. We then delivered patterns of doxycycline to the cell sheet to create controlled patterns of GFP expression. We show that the stability of the gene expression patterns in vitro heavily depends on the relationship between the dynamics of the inducible gene expression system and the dynamics of cellular rearrangements. By modeling the system we can predict the conditions that allow stable gene pattern formation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".