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Biological and synthetic membranes: What can be learned from a coarse-grained description?
Open accessAbstract
We discuss the role coarse-grained models play in the investigation of the structure and thermodynamics of bilayer membranes, and we place them in the context of alternative approaches. Because they reduce the degrees of freedom and employ simple and soft effective potentials, coarse-grained models can provide rather direct insight into collective phenomena in membranes on large time and length scales. We present a summary of recent progress in this rapidly evolving field, and pay special attention to model development and computational techniques. Applications of coarse-grained models to changes of the membrane topology are illustrated with studies of membrane fusion utilizing simulations and self-consistent field theory.
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Phase Segregation on Different Length Scales in a Model Cell Membrane System
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Mesoscopic simulations of phase transitions in lipid bilayers
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Calculation of the phase behavior of lipids
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Density-Dependent Potentials and the Hard-Sphere Model for Liquid Metals
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Pore Formation in Fluctuating Membranes
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Computer simulation of aqueous block copolymer assemblies: Length scales and methods
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Scaling behavior of fluid membranes in three dimensions
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Coarse-Grained Transmembrane Proteins: Hydrophobic Matching, Aggregation, and Their Effect on Fusion
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Metadata
- Authors
- MULLER, M., KATSOV, K., & SCHICK, M.
- Venue
- Physics Reports
- Publisher
- Elsevier BV
- Published
- 2006-11
- Volume
- 434
- Issue
- 5-6
- Pages
- 113-176
- ISSN
- 0370-1573 (ISSN-L)
- OpenAlex
- W2039981635
- Cited by
- 320
- References
- 365
- FWCI
- 8.60
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
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.