Mathematical model of GAL regulon dynamics in «Saccharomyces cerevisiae»
Bibliographic record
Abstract
Genetic switches are prevalent in nature, and provide cells with a strategy to adapt to changing environmental conditions. This thesis focuses on an intriguing example which is not understood in complete detail: the GAL switch. The GAL switch allows organisms to metabolize the sugar galactose, and controls whether the machinery responsible for the galactose metabolism is turned ON or OFF. Currently, it is not known exactly how the galactose signal is sensed by the transcriptional machinery. Moreover, there are two contradictory hypotheses concerning the regulatory mechanism at GAL promoters in galactose induced cells: the dissociation and non-dissociation models. This work uses quantitative tools to understand the Saccharomyces cerevisiae cell response to galactose challenge, and to analyze the plausible molecular mechanisms underlying its operation. The thesis proposes a novel dynamic mathematical model which is based on the interplay of the key regulatory proteins Gal4p, Gal80p, and Gal3p, in a cell population. To my knowledge, the deterministic model presented here is the first to reproduce qualitatively the bistable GAL network behavior found experimentally. Given the current understanding of the GAL circuit induction (Wightman et al., 2008; Jiang et al., 2009), this work proposes that the most likely in vivo mechanism leading to the transcriptional activation of the GAL genes is the physical interaction between galactose-activated Gal3p and Gal80p, with the complex Gal3p-Gal80p remaining bound at the GAL promoters. The mathematical model is in agreement with the flow cytometry profiles of wild type, gal3∆ and gal80∆ mutant strains from Acar et al. (2005), and involves a fraction of actively transcribing cells with the same qualitative features as in the data set collected by Acar et al. (2010). Furthermore, the computational modelling provides an explanation for the contradictory results obtained by independent laboratories when tackling experimentally the issue of binary versus graded GAL response to galactose induction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".