Constraints on Metabolic Network Analysis in Bacterial Physiology
Bibliographic record
Abstract
In biology, data are conceptualized using diagrams that capture protein-protein, enzyme-substrate, and regulator-target interactions, among many others. These interaction diagrams are every bit as complicated as wiring diagrams in modern electronic circuits—in many cases, even more so. Yet, in contrast to electronic circuits, living systems must also autonomously reproduce; some part of the “wiring diagram” of life must be devoted to reproducing itself. A greatly simplifying principle in the analysis of these biological wiring diagrams is that of “balanced growth,” in which network flows are balanced according to the requirements of biomass production, leading to the exponential accumulation of cells. In microbial cells, exponential growth greatly simplifies the underlying biochemical networks, because when its mathematical description is combined with kinetic descriptions of underlying enzyme-mediated reactions, macroscale constraints on physiology emerge. As is demonstrated in this tutorial, these constraints are mathematically and conceptually equivalent to Kirchhoff's circuit laws and Ohm's constitutive equation. Consequently, bacterial growth physiology can be approached with the same quantitative rigour as electrical circuit analysis. In this tutorial, this “Ohmics” approach is developed in detail, and its power in simplifying complex physiology is demonstrated through two case studies.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".