Phase Separation in Biology and Disease; Current Perspectives and Open Questions
Bibliographic record
Abstract
In the past almost 15 years, we witnessed the birth of a new scientific field focused on the existence, formation, biological functions, and disease associations of membraneless bodies in cells, now referred to as biomolecular condensates. Pioneering studies from several laboratories [reviewed in1, 2, 3] supported a model wherein biomolecular condensates associated with diverse biological processes form through the process of phase separation. These and other findings that followed have revolutionized our understanding of how biomolecules are organized in space and time within cells to perform myriad biological functions, including cell fate determination, signal transduction, endocytosis, regulation of gene expression and protein translation, and regulation of RNA metabolism. Further, condensates formed through aberrant phase transitions have been associated with numerous human diseases, prominently including neurodegeneration and cancer. While in some cases, rigorous evidence supports links between formation of biomolecular condensates through phase separation and biological functions, in many others such links are less robustly supported, which has led to rightful scrutiny of the generality of the roles of phase separation in biology and disease.4, 5, 6, 7 During a week-long workshop in March 2022 at the Telluride Science Research Center (TSRC) in Telluride, Colorado, ∼25 scientists addressed key questions surrounding the biomolecular condensates field. Herein, we present insights gained through these discussions, addressing topics including, roles of condensates in diverse biological processes and systems, and normal and disease cell states, their applications to synthetic biology, and the potential for therapeutically targeting biomolecular condensates.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".