Use of metabolic control analysis in lactation biology
Bibliographic record
Abstract
SUMMARY Sensitivity analysis is routinely carried out in the evaluation of simulation models to identify the degree to which parameters influence model outputs. This type of sensitivity analysis is much less frequently applied to real systems, but a technique called metabolic control analysis (MCA) was developed in the 1970s for the purpose of experimentally identifying the degree to which individual enzymes in a metabolic pathway influence flux through the pathway. MCA is applied to the results of inhibition, activation or genetic manipulation of enzymatic steps in a biochemical pathway. Flux control coefficients for each enzyme are defined as the fractional change in steady-state flux through the entire pathway for an infinitesimal change in the activity of that one enzyme. The sum of control coefficients in a linear, non-branching pathway is equal to one. It is a common finding in MCA that the control, or sensitivity, is distributed over multiple enzymes and not in a single rate-limiting enzyme. The fundamental principles of MCA are reviewed and an overview of experimental methods to measure control coefficients is provided, with the objective of introducing this approach to the fields of agricultural biochemistry and modelling, where it is little known. The application of MCA to the study of glucose metabolism and fatty acid synthesis in bovine mammary tissue are reviewed. The analyses indicated that mammary hexokinase activity exerts more control than transmembrane transport of glucose over lactose synthesis, and that control of cytosolic fatty acid synthesis is shared between acetyl-CoA carboxylase and fatty acid synthase, contrary to the widely held view that acetyl CoA carboxylase is the rate-limiting enzyme. It is suggested that MCA could be a valuable aid in the integration of proteomic and metabolomic data with metabolic flux measurements to engineer desired changes in the composition of milk from dairy animals.
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 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".