Linking modulation of bio-molecular phase behaviour with collective interactions
Bibliographic record
Abstract
Bio-molecular condensates formed in the cytoplasm of cells are increasingly recognised as key spatiotemporal organisers of living matter, and are implicated in a wide range of functional or pathological processes. This discovery opens up a new avenue for condensate-based applications and a crucial step in controlling this process is to understand the underlying interactions driving condensate formation or dissolution. However, these condensates are highly multi-component assemblies and many inter-component interactions are present, rendering it difficult to identify key promoters of phase separation. In this work, we extend the recently formulated dominance analysis to modulations of condensate formation. By carrying out dilute phase concentration measurements of a single target solute, the theoretical framework allows one to deduce whether the modulator acts on the target solute or another unspecified, auxiliary solute, as well as the attractive/repulsive nature of the added interaction. This serve as a general guide towards deducing possible modulation mechanisms on the molecular level, which can be complemented by orthogonal measurements. As a case study, we investigate the modulation of G3BP1/RNA condensates by the small molecule suramin, and the dominance measurements point towards a dissolution mechanism where suramin acts on G3BP1 to disrupt G3BP1/RNA interactions, as confirmed by a diffusional sizing assay. Our approach and the dominance framework have a high degree of adaptability and can be applied in many other condensate-forming systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".