Statistical mechanics of biomolecular condensates via cavity methods.
Molecular interaction
Statistical mechanics
Statistical physics
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
21 Apr 2023
21 Apr 2023
Historique:
received:
31
10
2022
revised:
23
12
2022
accepted:
24
02
2023
medline:
31
3
2023
entrez:
30
3
2023
pubmed:
31
3
2023
Statut:
epublish
Résumé
Physical mechanisms of phase separation in living systems play key physiological roles and have recently been the focus of intensive studies. The strongly heterogeneous nature of such phenomena poses difficult modeling challenges that require going beyond mean-field approaches based on postulating a free energy landscape. The pathway we take here is to calculate the partition function starting from microscopic interactions by means of cavity methods, based on a tree approximation for the interaction graph. We illustrate them on the binary case and then apply them successfully to ternary systems, in which simpler one-factor approximations are proved inadequate. We demonstrate the agreement with lattice simulations and contrast our theory with coacervation experiments of associative de-mixing of nucleotides and poly-lysine. Different types of evidence are provided to support cavity methods as ideal tools for modeling biomolecular condensation, giving an optimal balance between the consideration of spatial aspects and fast computational results.
Identifiants
pubmed: 36994084
doi: 10.1016/j.isci.2023.106300
pii: S2589-0042(23)00377-2
pmc: PMC10040705
doi:
Types de publication
Journal Article
Langues
eng
Pagination
106300Informations de copyright
© 2023 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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