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Cited by 648

The DOI matches its Crossref record; 125 of the 125 references it cites were found in a registry or an index.

A conceptual framework for understanding phase separation and addressing open questions and challenges

Open access
Mittag, Tanja, Pappu, Rohit V.·2022·Molecular Cell

Abstract

Macromolecular phase separation is being recognized for its potential importance and relevance as a driver of spatial organization within cells. Here, we describe a framework based on synergies between networking (percolation or gelation) and density (phase separation) transitions. Accordingly, the phase transitions in question are referred to as phase separation coupled to percolation (PSCP). Condensates that result from PSCP are viscoelastic network fluids. Such systems have sequence-, composition-, and topology-specific internal network structures that give rise to time-dependent interplays between viscous and elastic properties. Unlike pure phase separation, the process of PSCP gives rise to sequence-, chemistry-, and structure-specific distributions of clusters that can form at concentrations that lie well below the threshold concentration for phase separation. PSCP, influenced by specific vs. solubility-determining interactions, also provides a bridge between different observations and helps answer questions and address challenges that have arisen regarding the role of macromolecular phase separation in biology.

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Metadata

Authors
Mittag, T., & Pappu, R. V.
Venue
Molecular Cell
Publisher
Elsevier BV
Published
2022-06
Volume
82
Issue
12
Pages
2201-2214
ISSN
1097-2765 (ISSN-L)
OpenAlex
W4281688348
Cited by
648
References
125
FWCI
35.91
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: A conceptual framework for understanding phase separation and addressing open questions and challenges. https://app.cochat.ai/papers/10.1016/j.molcel.2022.05.018
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Mittag, T., & Pappu, R. V. (2022). A conceptual framework for understanding phase separation and addressing open questions and challenges. Molecular Cell. https://doi.org/10.1016/j.molcel.2022.05.018

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.