The big and intricate dreams of little organelles: Embracing complexity in the study of membrane traffic
Bibliographic record
Abstract
Compartmentalization of eukaryotic cells into dynamic organelles that exchange material through regulated membrane traffic governs virtually every aspect of cellular physiology including signal transduction, metabolism and transcription. Much has been revealed about the molecular mechanisms that control organelle dynamics and membrane traffic and how these processes are regulated by metabolic, physical and chemical cues. From this emerges the understanding of the integration of specific organellar phenomena within complex, multiscale and nonlinear regulatory networks. In this review, we discuss systematic approaches that revealed remarkable insight into the complexity of these phenomena, including the use of proximity-based proteomics, high-throughput imaging, transcriptomics and computational modeling. We discuss how these methods offer insights to further understand molecular versatility and organelle heterogeneity, phenomena that allow a single organelle population to serve a range of physiological functions. We also detail on how transcriptional circuits drive organelle adaptation, such that organelles may shift their function to better serve distinct differentiation and stress conditions. Thus, organelle dynamics and membrane traffic are functionally heterogeneous and adaptable processes that coordinate with higher-order system behavior to optimize cell function under a range of contexts. Obtaining a comprehensive understanding of organellar phenomena will increasingly require combined use of reductionist and system-based approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".