Mathematical and numerical modelling of copper transport in yeast
Bibliographic record
Abstract
Abstract The transport and regulation of metals in eukaryotic cells is a complex process, dependent on protein transporters that respond to cell needs. The application of dynamic mathematical models can provide valuable insights into these transport mechanisms. Mathematical simulations of transport processes may not directly predict transport mechanisms but can guide experimental design or identify inconsistencies between observation and hypotheses. Copper is an essential metal in eukaryotic cells as a catalytic co-factor in metallochaperone proteins and is therefore tightly regulated in living systems, making it valuable for quantifying biological transport mechanisms. In order to test our modeling system, a culture of baker’s yeast (Saccharomyces cerevisiae) was grown, copper concentrations were obtained from the cells and growth media, and a mathematical model was developed to investigate transport mechanisms between the growth media and the cells. A model based on conservation of mass was presented as a system of equations upon which to develop. This system of equations was developed to include an active transport term that describes a homeostatic concentration that cells actively maintain through negative feedback, and with a delayed activation, the model was more accurate at predicting the experimental data. The hypothesis and dynamic model derived in this work provide a novel framework that may be applied to additional metals or used to describe other transport mechanisms in biological systems.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".