Design principles underlying nearly-homeostatic biological networks
Bibliographic record
Abstract
Abstract A nearly-homeostatic system like body temperature maintenance keeps the steady state system output like internal body temperature within a narrow range, regardless of different persistent levels of environmental perturbations like external temperatures. Nearly-homeostatic systems can be implemented to guarantee performance of a therapeutic device in different patient contexts. Exploration of different near-homeostasis-supporting architectures to satisfy different design requirements is necessary, but methods for doing so in a fast and comprehensive manner remain elusive. We have identified two constrained optimization approaches to find near-homeostasis supporting architectures 10 to 100 times faster than brute-force search. Once such architectures are found, characterizing the underlying mechanisms of near-homeostasis is hindered by the very cumbersome and limited nature of traditional statistical analysis approaches. We have developed two levels of “inverse homeostasis plots”, and used them to identify two novel near-homeostasis mechanisms that tolerate much higher undesired basal expression and much higher undesired first order degradation rates than previously characterized near-homeostasis mechanisms. Teaser A comprehensive toolset was developed for much faster discovery and much more comprehensive analysis of near-homeostasis-supporting architectures.
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 | 0.001 | 0.003 |
| 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) | 0.003 | 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".