Thermodynamic constraints are sufficient for the emergence of flux sensors in metabolism
Bibliographic record
Abstract
Abstract Metabolism is a precisely coordinated phenomenon, the apparent goal of which is to balance fluxes to maintain robust growth. However, coordinating fluxes requires information about rates , which is not obviously reconcilable with known regulatory mechanisms in which concentrations are sensed through metabolite binding. While flux sensor examples have been characterized, the fundamental principles underlying the phenomenon in general are not well understood. Specifically, the questions of which fluxes can be sensed, and the mechanism by which they are remain open. We address this by showing that the concentrations of substrates of thermodynamically constrained reactions reflect upstream flux and therefore carry information about rates which can be propagated through regulatory interactions to control other fluxes in the network. Using fluxomic, metabolomic, and thermodynamic data in E coli , we show that the concentrations of a few metabolites in central carbon metabolism reflect their producing fluxes and demonstrate that they can transmit information about these rates because of their positions in the network and their roles as effectors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".