ODE-based models of signaling networks in autophagy
Bibliographic record
Abstract
Aberrant metabolism and nutrient processing are hallmarks in cancer. Autophagy is a catabolic process, clearing macromolecules and providing metabolite intermediates for anabolism. Autophagy safeguards healthy cells from tumorigenesis while mobilizing metabolites promoting tumor growth. Autophagy is controlled by the mTOR signaling network in conjunction with AMPK and ULK1. This kinase triad features highly intertwined feedback and feedforward mechanisms, complicating predictions on nutrient and drug response. ODE-based models offer a deterministic approach frequently used for the exploration of signaling dynamics. Recent ODE models of the mTOR-AMPK-ULK1 network revealed non-linear behaviors, bistable switches, and oscillatory patterns, shedding light on the robustness and adaptability of autophagy control. We highlight emerging perspectives on AMPK in mTORC1-ULK1 crosstalk and mechanisms for integration into future models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".