AGING AND FRAILTY IN SILICO: THE PROPAGATION OF LOCAL DAMAGE THROUGH COMPLEX NETWORKS
Bibliographic record
Abstract
Background: The relationships between aging, frailty and mortality are well documented but remain mainly empirical. We present a dynamical network model that explains the patterns of mortality and frailty. Methods: We developed a computational model of human organism as a complex dynamical network of interacting nodes. Each individual is represented by a distinct network with 10,000 nodes- representing potential health deficits that can be in one of two states for every individual: either healthy, or damaged. Some have more connections than others, and each has a local environment defined by the damage state of its connected nodes. The local damage affects the nodes connected with that one (e.g. enhances the damage rate and reduces the repair rate of these nodes). Using Shannon entropy, we calculated how much information frailty adds to assessing the risk of death and how much information individual deficits add. Results: Transitions between damaged and non-damaged states are governed by their stochastic environment. Our model shows how age-dependent acceleration of the frailty index and the Gompertz law of mortality emerge, without specifying an age-damage relationship. The mortality prediction information added by specific deficits increases with deficit degree, i.e. with the number of connections with other deficits: the most connected deficits (e.g. disabilities) become damaged later in life, in contrast to the least connected deficits. Conclusions: Our model supports the idea that aging occurs as an emergent phenomenon, and not as the result of age-specific genetic programming. Instead, aging reflects how damage propagates through a complex network of interconnected elements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".