Evolution in silico of genes with multiple regulatory modules on the example of the Drosophila segmentation gene hunchback
Bibliographic record
Abstract
We use in silico evolution to study the generation of gene regulatory structures. A particular area of interest in evolutionary development (evo-devo) is the correspondence between gene regulatory sequences on the DNA (cis-regulatory modules, CRMs) and the spatial expression of the genes. We use computation to investigate the incorporation of new CRMs into the genome. Simulations allow us to characterize different cases of CRM to spatial pattern correspondence. Many of these cases are seen in biological examples; our simulations indicate relative advantages of the different scenarios. We find that, in the absence of specific constraints on the CRM-pattern correspondence, CRMs controlling multiple spatial domains tend to evolve very quickly. Genes constrained to a one-to-one CRM-pattern domain correspondence evolve more slowly. Of these, systems in which pattern domains appear in a particular order in evolution, as in insect segmentation mechanisms, take the longest time in in silico evolutionary searches. For biological cases of this type, it is likely that other selective advantages outweigh the time costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".