Ensemble Modeling of Cancer Metabolism
Bibliographic record
Abstract
The metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire ensemble of models to obtain a sub-set, which is increasingly predictive. EM allows for incorporation of regulatory information and captures the behavior of enzymatic reactions at the molecular level by representing reactions in the elementary reaction form. In this study, a metabolic network consisting of 58 reactions is considered and accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation of key enzymes. Experimentally measured intracellular and extracellular metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinyl-CoA ligase (SUCOAS1m) to cause a significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".