Designing experiments from noisy metabolomics data to refine constraint-based models
Bibliographic record
Abstract
Metabolomics is an emerging technology to make high-throughput measurements of metabolites and is useful for the discovery of novel biomarkers of genetic diseases and for metabolic engineering. The system-wide data can be used to refine predictions made by constraint-based models of cell metabolism. However, the predictions of important output variables may still suffer from high variability due to high variance in the data itself, or from suboptimal choice of measurements in the metabolomics experiment. Here, we present a computational algorithm that uses initial metabolomics data to identify a smaller set of metabolites whose precise measurement most reduces variability of model predictions. We first randomly sample fluxes and concentrations using a new non-convex sampling algorithm that differs from previous approaches in its ability to sample across disjoint regions of the space and in its parallel implementation. We then demonstrate our algorithm's ability to identify a sequence of experiments that successively refines model predictions using a simplified model of Escherichia coli central metabolism.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".