Spatiotemporal control of gene expression boundaries using a feedforward loop
Bibliographic record
Abstract
A feed forward loop (FFL) is commonly observed in several biological networks. The FFL network motif has been mostly been studied with respect to variation of the input signal in time, with only a few studies of FFL activity in a spatially distributed system such as morphogen‐mediated tissue patterning. However, most morphogen gradients also evolve in time. We studied the spatiotemporal behavior of a coherent FFL in two contexts: (1) a generic, oscillating morphogen gradient and (2) the dorsal‐ventral patterning of the early Drosophila embryo by a gradient of the NF‐κB homolog Dorsal with its early target Twist. In both models, we found features in the dynamics of the intermediate node – phase difference and noise filtering – that were largely independent of the parameterization of the models, and thus were functions of the structure of the FFL itself. In the Dorsal gradient model, we also found that the dynamics of Dorsal require maternal pioneering factor Zelda for proper target gene expression. Support or Funding Information During this work, GTR and PB were partially supported by NIH grant (number R21HD092830), and GTR and HA were partially supported by NSF CAREER award (number CBET‐1254344). A . Schematic figure depicting the generic morphogen gradient model. The inset shows the variation of the concentration of the primary transcription factor A in space and time. B . Schematic figure depicting the Dl/Twi/Zld model. The inset shows the variation of the concentration of the primary transcription factor Dl in space and time. The peaks in the Dl gradient amplitude in time correspond to nuclear cycles 11–14. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".