Analyses of alternative cell signal transduction pathways
Bibliographic record
Abstract
Living cells keep sensing the changes in their environments, mostly, via cell surface receptors for different ligands. Attachment-dependent cells are sensitive to alterations in extracellular matrix (ECM). ECM is not only required for cell survival, but also prerequisite for epidermal growth factor (EGF) to stimulate cell proliferation. The receptors for the majority of ECM components are integrins and the receptor for EGF is EGF receptor (EGFR). When bound by their ligands, integrins and EGFR induce signal transduction cascades composed of alternative pathways. A quantitative assessment of relative contributions of alternative pathways to one final cell signaling will help understand designing principles of the network. Unfortunately, a methodology for such assessment is still not available, partly because of lack of relatively mature mathematical models. On the other hand, in most biochemical cascades, existence of alternative pathways increases the complexity and thus the robustness of networks. The relationships between the topology and robustness of large-scale biochemical networks have been studied intensively recently. In small-scale networks, while feedback has been revealed as an important contributor for adaptation and robustness, the quantitative correlation between the topology/pathway redundancy of small networks and their robustness remains unknown.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".