Ageing signalling pathways: Quantifying the effects of protein interactions
Bibliographic record
Abstract
Cells adapt their metabolism and activities in response to signals from their surroundings, and this ability is essential for their survival in the face of environmental changes. In mammalian tissues a deficit of these mechanisms is commonly associated with cellular aging and degenerative diseases related to aging, such as cardiovascular disease, cancer, immune system decline, and neurological pathologies. Several proteins have been identified as being able to respond directly to energy, nutrient, and growth factor levels and stress stimuli in order to mediate adaptations in the cell. In particular, mTOR, AMPK, and sirtuins are known to play an essential role in the management of metabolic stress and in the mechanisms and metabolism of ageing of mammals. Given that the ageing process remains to be completely understood, the goal of this study is to gain insights into the interactions of the proteins in the ageing‐related signalling pathways, and how those interactions may impact longevity and related diseases. To achieve that goal, we have developed a computational model of ageing signalling pathways. Specifically, the insulin growth factor/mTOR pathway and the sirtuin pathway are represented. The model simulates the interactions among Akt, mTORC1, AMPK, NAD, and SIRT, and predicts their dynamics. The model can be used as an essential component to simulate gene manipulation, therapies (e.g., resveratrol and curcumin), calorie restrictions, and chronic stress, and assess their functional implications on longevity and ageing‐related diseases. Support or Funding Information This research was supported in part by the Canada 150 Research Chair program.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".