Investigating glucose-lactate metabolism in glioblastoma multiforme via universal physics-informed neural networks
Bibliographic record
Abstract
Understanding the metabolic adaptations of cancer cells is crucial for uncovering potential therapeutic targets and improving treatment strategies. In this study, we present a hybrid modeling framework that combines Physics-Informed Neural Networks (PINNs) and Universal PINNs (UPINNs) to investigate glucose-lactate metabolism in glioblastoma cell lines. We first employed PINNs to infer critical model parameters governing glucose uptake and phenotypic switching in tumor cells, demonstrating high accuracy using synthetic data. We then extended this framework using UPINNs to uncover hidden metabolic dynamics that could not be explicitly modeled, introducing a latent variable $ W $ to represent unknown functional behavior in glycolytic processes. Our approach was validated for both synthetic and experimental datasets for two glioblastoma cell lines (LN18 and LN229) with distinct metabolic phenotypes. The UPINN framework not only captured cell-type-specific behaviors but also remained robust in the presence of moderate experimental noise. Furthermore, we explored the sensitivity of the model to the trade-off between data fidelity and mechanistic constraints, showing that the choice of loss term weighting significantly impacts predictive performance. While our application centered on cancer metabolism, the proposed method was general and applicable to a wide range of systems described by differential equations, including problems in biology, engineering, and physical sciences. This work demonstrates the potential of UPINNs as a powerful and interpretable tool for data-driven discovery in partially observed dynamical systems.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".