Proteomics and Metaproteomics Add Functional, Taxonomic and Biomass Dimensions to Modeling the Ecosystem at the Mucosal-luminal Interface
Bibliographic record
Abstract
Recent efforts in gut microbiome studies have highlighted the importance of explicitly describing the ecological processes beyond correlative analysis. However, we are still at the early stage of understanding the organizational principles of the gut ecosystem, partially because of the limited information provided by currently used analytical tools in ecological modeling practices. Proteomics and metaproteomics can provide a number of insights for ecological studies, including biomass, matter and energy flow, and functional diversity. In this Mini Review, we discuss proteomics and metaproteomics-based experimental strategies that can contribute to studying the ecology, in particular at the mucosal-luminal interface (MLI) where the direct host-microbiome interaction happens. These strategies include isolation protocols for different MLI components, enrichment methods to obtain designated array of proteins, probing for specific pathways, and isotopic labeling for tracking nutrient flow. Integration of these technologies can generate spatiotemporal and site-specific biological information that supports mathematical modeling of the ecosystem at the MLI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".