The DOI matches its Crossref record; 171 of the 171 references it cites were found in a registry or an index.
Giant Vesicles: Preparations and Applications
Open accessAbstract
There is considerable interest in preparing cell‐sized giant unilamellar vesicles from natural or nonnatural amphiphiles because a giant vesicle membrane resembles the self‐closed lipid matrix of the plasma membrane of all biological cells. Currently, giant vesicles are applied to investigate certain aspects of biomembranes. Examples include lateral lipid heterogeneities, membrane budding and fission, activities of reconstituted membrane proteins, or membrane permeabilization caused by added chemical compounds. One of the challenging applications of giant vesicles include gene expressions inside the vesicles with the ultimate goal of constructing a dynamic artificial cell‐like system that is endowed with all those essential features of living cells that distinguish them from the nonliving form of matter. Although this goal still seems to be far away and currently difficult to reach, it is expected that progress in this and other fields of giant vesicle research strongly depend on whether reliable methods for the reproducible preparation of giant vesicles are available. The key concepts of currently known methods for preparing giant unilamellar vesicles are summarized, and advantages and disadvantages of the main methods are compared and critically discussed.
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Metadata
- Authors
- Walde, P., Cosentino, K., Engel, H., & Stano, P.
- Venue
- ChemBioChem
- Publisher
- Wiley
- Published
- 2010-04-27
- Volume
- 11
- Issue
- 7
- Pages
- 848-865
- ISSN
- 1439-4227 (ISSN-L)
- 1439-7633
- OpenAlex
- W2130531386
- Cited by
- 770
- References
- 171
- FWCI
- 17.72
- Type
- journal article
- Language
- en
- Open access
- green
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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.
