Applying an inverse homeostasis perspective to simplify the design and implemention of robustly-nearly-homeostatic biological networks
Bibliographic record
Abstract
Abstract A nearly-homeostatic system is able to maintain the steady state system output close to a fixed set point, despite varying levels of environmental perturbation. Studying such systems enables the de- sign of synthetic cellular systems able to function consistently under a wide range of environmental conditions. Here we present the inverse homeostasis perspective, a novel approach to studying and de- signing nearly-homeostatic systems. It represents a graphical approach allowing visualization of how each regulatory parameter affects near-homeostatic performance while being more accessible than situation- specific, mathematically complex approaches. Given the difficulty of precise parameter measurement in biology, we focus on adjusting experimentally-determined steady state response curves to implement near- homeostatic systems without requiring explicit parameter measurements. One implication of the inverse homeostasis perspective is an illustration of the much stronger dependence of nearly-perfect integral con- trollers on the properties of the existing system being controlled, compared to the system-independence of perfect integral controllers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".