Modeling signaling processes across cellular membranes using a mesoscopic approach

George Khelashvili*, Daniel Harries

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Scopus citations


Computational models are effective in providing quantitative predictions on processes across cellular membranes, thereby aiding experimental observations. Conventional computational tools, such as molecular dynamics or Monte Carlo simulation, offer significant insights when applicable. However, it remains extremely difficult to use these simulation methods to describe large macromolecular assemblies within timescales relevant to a vast majority of critical physiological processes. To overcome this outstanding challenge, alternative methods based on coarse-grained representations have more recently emerged. In this chapter, we review one such particular advanced methodology that is based on mean-field-type representations typically used for equilibrium thermodynamic calculations of lipids and proteins. The main advantages of this self-consistent scheme are in adding information concerning longer timescales and in gaining access to the steady state of the system without making a priori assumptions concerning protein-membrane interactions. We illustrate this methodology using several examples pertaining to interactions of peripheral signaling proteins with lipid membranes. These examples outline the current state of the computational strategy and allow us to discuss several future enhancements that should help the scheme become a powerful methodology complementary to other simulation techniques. With these extensions, the proposed methodology could enable quantitative description of large-scale membrane-associated interactions that are of major importance in physiological processes of the healthy and diseased cell.

Original languageAmerican English
Title of host publicationAnnual Reports in Computational Chemistry
PublisherElsevier BV
Number of pages26
StatePublished - 2010

Publication series

NameAnnual Reports in Computational Chemistry
ISSN (Print)1574-1400

Bibliographical note

Funding Information:
We thank Nathan Baker, Michael Holst, and Todd Dolinsky for their advice on modifying the APBS code, as well as Harel Weinstein, Jim Sethna, Adrian Parsegian, David Andelman, and Brian Todd for valuable comments on the original manuscripts describing our method. GK is supported by grants from the National Institutes of Health P01 DA012408 and P01 DA012923. DH acknowledges support from the Israel Science Foundation (ISF Grant No. 1011/07) as well as an allocation for a high-performance computer cluster facility (ISF Grant No. 1012/07). The Fritz Haber research center is supported by the Minerva Foundation, Munich, Germany. Computational resources of the David A. Cofrin Center for Biomedical Information in the HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine are gratefully acknowledged.


  • BAR domains
  • Cahn-Hilliard equations
  • Cell signaling
  • Coarse-grained theory
  • Lipid rafts
  • Mean-field model
  • Membrane curvature
  • Membrane elasticity
  • PIP diffusion
  • Poisson-Boltzmann theory


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