The DOI matches its Crossref record; 645 of the 645 references it cites were found in a registry or an index.
Computational Modeling of Realistic Cell Membranes
Open accessAbstract
Cell membranes contain a large variety of lipid types and are crowded with proteins, endowing them with the plasticity needed to fulfill their key roles in cell functioning. The compositional complexity of cellular membranes gives rise to a heterogeneous lateral organization, which is still poorly understood. Computational models, in particular molecular dynamics simulations and related techniques, have provided important insight into the organizational principles of cell membranes over the past decades. Now, we are witnessing a transition from simulations of simpler membrane models to multicomponent systems, culminating in realistic models of an increasing variety of cell types and organelles. Here, we review the state of the art in the field of realistic membrane simulations and discuss the current limitations and challenges ahead.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015) · SoftwareX · doi:10.1016/j.softx.2015.06.001 · Cited by 27,190
Development and testing of a general amber force field
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A., & Case, D. A. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20035 · Cited by 19,506
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
GROMACS: A message-passing parallel molecular dynamics implementation
Berendsen, H. J. C., van der Spoel, D., & van Drunen, R. (1995) · Computer Physics Communications · doi:10.1016/0010-4655(95)00042-e · Cited by 10,405
The Amber biomolecular simulation programs
Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20290 · Cited by 9,535
CHARMM‐GUI: A web‐based graphical user interface for CHARMM
Jo, S., Kim, T., Iyer, V. G., & Im, W. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.20945 · Cited by 9,284
CHARMM: The biomolecular simulation program
Brooks, B. R., Brooks, C. L., Mackerell, A. D., Nilsson, L., Petrella, R. J., Roux, B., Won, Y., Archontis, G., Bartels, C., Boresch, S., et al. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21287 · Cited by 8,939
GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit
Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D., et al. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt055 · Cited by 7,473
Membrane lipids: where they are and how they behave
van Meer, G., Voelker, D. R., & Feigenson, G. W. (2008) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2330 · Cited by 7,157
Cited by
Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development
Salo-Ahen, O. M. H., Alanko, I., Bhadane, R., Bonvin, A. M. J. J., Honorato, R. V., Hossain, S., Juffer, A. H., Kabedev, A., Lahtela-Kakkonen, M., Larsen, A. S., et al. (2020) · Processes · doi:10.3390/pr9010071 · Cited by 659
Emerging Diversity in Lipid–Protein Interactions
Corradi, V., Sejdiu, B. I., Mesa-Galloso, H., Abdizadeh, H., Noskov, S. Y., Marrink, S. J., & Tieleman, D. P. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00451 · Cited by 459
Self-citationMachine Learning for Electronically Excited States of Molecules
Westermayr, J., & Marquetand, P. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00749 · Cited by 398
Two decades of Martini: Better beads, broader scope
Marrink, S. J., Monticelli, L., Melo, M. N., Alessandri, R., Tieleman, D. P., & Souza, P. C. T. (2023) · WIREs Computational Molecular Science · doi:10.1002/wcms.1620 · Cited by 255
Self-citationMolecular dynamics simulation of an entire cell
Stevens, J. A., Grünewald, F., van Tilburg, P. A. M., König, M., Gilbert, B. R., Brier, T. A., Thornburg, Z. R., Luthey-Schulten, Z., & Marrink, S. J. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2023.1106495 · Cited by 156
“Lastly, modeling lipid membranes has historically been a leading application of the Martini force fi eld (Marrink et al., 2019).”
MethodSelf-citation
Metadata
- Authors
- Marrink, S. J., Corradi, V., Souza, P. C. T., Ingólfsson, H. I., Tieleman, D. P., & Sansom, M. S. P.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-01-09
- Volume
- 119
- Issue
- 9
- Pages
- 6184-6226
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W2910356247
- Cited by
- 694
- References
- 645
- FWCI
- 6.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Computational Modeling of Realistic Cell Membranes. https://app.cochat.ai/papers/10.1021/acs.chemrev.8b00460
- The paper
- Marrink, S. J., Corradi, V., Souza, P. C. T., Ingólfsson, H. I., Tieleman, D. P., & Sansom, M. S. P. (2019). Computational Modeling of Realistic Cell Membranes. Chemical Reviews. https://doi.org/10.1021/acs.chemrev.8b00460
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.