Structural analysis of Petri nets for modeling and analyzing signaling pathways
Bibliographic record
Abstract
In this paper we present a Petri net model of Epidermal Growth Factor Receptor (EGFR) signaling to the Ras-Raf-Mek-Erk (Ras-MAPK) pathway. The EGFR-Ras-MAPK pathway has been strongly implicated in the development and progression of cancer. This has prompted the development of drugs targeting oncogenic proteins within this signaling pathway. However, the efficacy of these and other anti-cancer drugs has been hampered by the emergence of drug resistance. This has fuelled initiatives in developing combination therapies to concurrently target more than one oncogenic protein at a time. A central challenge of these multi-target therapy initiatives lie in identifying sets of targets which upon inhibition, provide improved therapeutic benefit over inhibiting the same targets alone. Physiochemical models based on ordinary differential equations (ODE) have been commonly employed to identify candidate drug target(s). However, the amount of knowledge required to dynamically model large signaling networks is currently lacking. This shortcoming has impeded attempts to use ODE-based models for identifying drug combinations. In this paper, we have implemented a Petri-net model of EGFR-Ras-MAPK signaling. An analytical method has been employed to identify nodes within the model from which a signal is irrecoverable once lost. Such nodes, called siphons, represent candidate drug target(s) for combination therapy. The potential utility of this method is highlighted by the identification of siphons composed of oncogenic nodes currently targeted by drugs in the clinic. Importantly, the method relies solely on structural information and constitutes a potentially valuable tool that could be scaled up to identify `targetable' combinations in larger biochemical signaling networks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".