Gene regulatory network (GRN) embedded agents connect cellular decision making to human pluripotent stem cell derived germ layer-like pattern formation
Bibliographic record
Abstract
ABSTRACT The emergence of germ layers in embryos during gastrulation is a key developmental milestone. How morphogenetic signals engage the regulatory networks responsible for early embryonic tissue patterning is incompletely understood. To understand this, we developed a gene regulatory network (GRN) model of human pluripotent stem cell (hPSC) lineage commitment and embedded it into ‘cellular’ agents that respond to a dynamic signalling microenvironment. We found that cellular pattern order, composition, and dynamics were predictably manipulable based on the GRN wiring. We showed that feedback between OCT4, and BMP and WNT pathways created a dynamic OCT4 front that mediates the spatiotemporal evolution of developmental patterns. Translocation of this radial front can be predictively disrupted in vitro to control germ-layer pattern composition. This work links the emergence of multicellular patterns to regulatory network activity in individual hPSCs. We anticipate our approach will help to understand how GRN structure regulates organogenesis in different contexts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".