THE DYNACTOME: INVESTIGATING DYNAMIC KINASE NETWORKS IN CELLULAR DECISION-MAKING
Bibliographic record
Abstract
Networks of kinases play a role in the transmission and integration of signals from the membrane to the nucleus. We aim to elucidate kinase phosphorylation and interaction partners in these networks through the immuno-precipitation and mass spectrometric analysis of a representative set of 100 Flag-tagged kinases stably expressed in human colorectal cancer cells. The goal is to generate a comprehensive set of interactions and dynamic phosphorylation sites which correlate with cell phenotypes such as apoptosis and proliferation. The techniques of mass-spectrometry have allowed for the identification of proteins and their phosphorylation sites in complex samples. Various labeling methods such as iTRAQ has enabled the relative quantification of these sites as afunction of time (White et al. PNAS, 2007). However, kinases usually work in the context of particular signaling stimuli. We aim to characterize the role of these over-expressed kinases in the context of Trail-induced apoptosis. This isparticularly relevant to tumorigenesis in that many cancers are resistant to apoptosis and recombinant Trail therapies are currently undergoing clinical trials. We present assays to correlate the proliferative ability and sensitivity to apoptosis of various stable cell lines with kinase expression levels through flow cytometry. We also present efforts to trace downstream signaling through the monitoring of MAP kinase phosphorylation using a high-throughput bead array.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".