Neural tube patterning: from a minimal model for rostrocaudal patterning towards an integrated 3D model
Bibliographic record
Abstract
Abstract The rostrocaudal patterning of the neural tube is a key event in early brain development. This process is mainly driven by a gradient of WNT, which defines the fate of the present neural progenitor cells in a dose dependent matter and leads to a subdivision of the tube into forebrain, midbrain and hindbrain. Although this process is extensively studied experimentally both in vivo and in vitro , an integrated view of the responsible genetic circuitry is currently lacking. In this work, we present a minimal gene regulatory model for rostrocaudal neural tube patterning. The model's nodes and architecture are determined in a data driven way, leading to a tristable configuration of mutually repressing brain regions. Analysis of the parameter sensitivity and simulations of knockdown and overexpression cases show that repression of hindbrain fate is a promising strategy for the improvement of current protocols for the generation of dopaminergic neurons in vitro . Furthermore, we combine the model with an existing model for dorsoventral neural tube patterning, to test its capabilities in an in vivo setting, by predicting the steady state pattern of a realistic three-dimensional neural tube. This reveals that the rostrocaudal pattern stacks dorsoventrally in the caudal half of the neural tube. Finally, we simulate morphogen secretion overexpression, which highlights the sensitivity of neural tube patterning to the morphogen levels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".