How to turn an organism into a model organism in 10 ‘easy’ steps
Bibliographic record
Abstract
ABSTRACT Many of the major biological discoveries of the 20th century were made using just six species: Escherichia coli bacteria, Saccharomyces cerevisiae and Schizosaccharomyces pombe yeast, Caenorhabditis elegans nematodes, Drosophila melanogaster flies and Mus musculus mice. Our molecular understanding of the cell division cycle, embryonic development, biological clocks and metabolism were all obtained through genetic analysis using these species. Yet the ‘big 6’ did not start out as genetic model organisms (hereafter ‘model organisms’), so how did they mature into such powerful systems? First, these model organisms are abundant human commensals: they are the bacteria in our gut, the yeast in our beer and bread, the nematodes in our compost pile, the flies in our kitchen and the mice in our walls. Because of this, they are cheaply, easily and rapidly bred in the laboratory and in addition were amenable to genetic analysis. How and why should we add additional species to this roster? We argue that specialist species will reveal new secrets in important areas of biology and that with modern technological innovations like next-generation sequencing and CRISPR-Cas9 genome editing, the time is ripe to move beyond the big 6. In this review, we chart a 10-step path to this goal, using our own experience with the Aedes aegypti mosquito, which we built into a model organism for neurobiology in one decade. Insights into the biology of this deadly disease vector require that we work with the mosquito itself rather than modeling its biology in another species.
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".