Rewiring of Transcriptional Regulatory Networks: Hierarchy, Rather Than Connectivity, Better Reflects the Importance of Regulators
Bibliographic record
Abstract
Network connectivity has been related to essentiality: Highly connected proteins (hubs) are more important for cell growth and survival. Although this is intuitively reasonable, another way to assess the role of a regulator is to assign it to a level within a "chain-of-command" hierarchy. Here, we analyzed the effects of network rewiring events on transcriptional regulatory hierarchies in two species. First, we superimposed the phenotypic effects of tampering with specific genes and their regulatory connections directly onto the hierarchies. To study second-order effects, which involved changes in the level of regulators within the hierarchy upon deletions or insertions of other regulators or connections, we reconstructed modified hierarchies. We found that rewiring events that affected upper levels had a more marked effect on cell proliferation rate and survival than did those involving lower levels. Moreover, we showed that the hierarchical level and type of change better reflected the phenotypic effect of rewiring than did the number of changes. We also investigated other features connected to the importance of upper-level regulators: In particular, relative to lower-level regulators, upper-level regulators exhibited a greater range of expression values across species, had fewer functionally redundant copies, and had a shorter half-life. Overall, our analysis shows that broadly constructed hierarchies may better reflect the importance of regulators for cell growth than classifications based on the number of connections (hubbiness).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".