Homeostatic set-points are physical and foundational to organism autonomy
Bibliographic record
Abstract
Living systems pose the problem of how a physical system not only maintains itself in a ‘far from equilibrium’ state, but how it determines what state that should be and how it could take the initiative over setting the state, in an act of autonomy that confounds standard physical description. Here I show that the homeostatic set-point is physical information embodied in the structure of evolved enzymes, acting through a series of code and cypher transformations from gene translation, through single molecule dynamics, reaction kinetics, to control system topology and finally physical control. In ‘kinetic regulation’, network topology is determined by the parameters of reaction kinetics and jointly with them, specifies the set-point. Control is added via allosteric manipulation of enzymes based on evolved, not physically inevitable, relationships with effector molecules. This way, enzyme properties alone can determine the set-point. Thus the allosteric enzyme is to homeostasis what the adaptor molecule is to biological code. All the parameters of biochemical regulation result from the way protein structure determines binding affinity and dissociation rates via the shape of electrical forcefields, especially around active sites and effector pockets. In general, the set-point is a highly evolved phenotype having a complicated, but logically inevitable dependence on genotype. By embodying particularising information (the set-point) and using it to do organisational work (homeostasis), even the simplest living system enacts cybernetic independence, characterised by goal-directed behaviour, not found in natural abiotic systems. • Homeostatic set-points are information encoded by system topology plus molecular form. • System topology is encoded by molecular forms fitting together like jigsaw pieces. • Molecular forms are physical information translated by genetic code and folding. • Allosteric enzymes are to homeostasis what adaptor molecules are to biological code. • Set-points are the primary physical information of self-control in living systems.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".