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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".