Modeling signaling processes across cellular membranes using a mesoscopic approach

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.