Evolutionary cell biology comes of age
Bibliographic record
Abstract
Evolutionary cell biology is emerging as a vibrant discipline, integrating comparative cell biology, evolutionary theory and modern molecular approaches to understand how cells evolve and diversify. With roots dating back to the foundational work of Darwin and Haeckel in the 1800s, the field was historically eclipsed by a focus on a handful of genetically tractable model organisms. Yet, breakthroughs in genomics, imaging, experimental evolution and phylogenetics are driving the rapid growth of the field. Modern evolutionary cell biology faces four central challenges: integrating cell biology with evolutionary theory and experimental evolution to understand both adaptive and non-adaptive processes, bridging the genotype-phenotype gap, identifying and developing new model systems beyond traditional organisms to capture the full diversity of cellular mechanisms, and integrating ecological context with evolutionary processes to understand how environmental forces shape cellular phenotypes. In this Perspective, we discuss how meeting these challenges will illuminate fundamental evolutionary rules governing cellular complexity, innovation and adaptation across the tree of life, with potential applications for predicting cellular responses to future environmental challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".