Metabolic Buffer Analysis Reveals the Simultaneous, Independent Control of ATP and Adenylate Energy Ratios
Bibliographic record
Abstract
Abstract Determining the underlying principles behind biological regulation is important for understanding the principles of life, treating complex diseases, and creating de novo synthetic biology. Buffering - the use of reservoirs of molecules to maintain molecular concentrations - is a widespread and important mechanism for biological regulation. However, a lack of theory has limited our understanding of its roles and quantified effects. Here, we study buffering in energy metabolism using control theory and novel buffer analysis. We find that buffering can enable the simultaneous, independent control of multiple coupled outputs. In metabolism, adenylate kinase and AMP deaminase enable simultaneous control of ATP and adenylate energy ratios, while feedback on metabolic pathways is fundamentally limited to controlling one of these outputs. We also quantify the regulatory effects of the phosphagen system - the above buffers and creatine kinase - revealing which mechanisms regulate which outputs. The results are supported by human muscle and mouse adipocyte data. Together, these results illustrate the synergy of feedback and buffering in molecular biology to simultaneously control multiple outputs.
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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 | 0.000 | 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) | 0.001 | 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".