ABSTRACT
Membranes play a vital role in many biological processes. They
separate cytoplasm from extracellular environment and provide
basic compartmentalization of intra-cellular processes. They also
regulate the exchange of material and information between the
enclosed cell and its environment. Biomembranes’ dynamic and
structural properties as well as their interaction with other cellular
components, such as membrane proteins, have long been an active
research field. The motions of biomembranes span a wide range
of temperal and spatial scales (Jacobson et al., 2007; Phillips
et al., 2009; Vereb et al., 2003), angstrom to micron, picosecond to
microsecond. Various computational models have been developed
to gain insight in the multiscale motions of biomembranes. The all-
atom force fields such as GROMACS (Berger et al., 1997; Chiu et al.,
2009) and CHARMM(Feller andMacKerell, 2000; Klauda et al., 2012,
2010; Lim et al., 2012) give details of atomic interactions between
membrane lipids and proteins. At the other extreme are finite-
element models that describe large-scale mechanical properties of
membranes (Chen et al., 2008; Ma et al., 2009; Tang et al., 2006,
2008). The all-atom and continuum models represent two ends of
a spectrum of multiscale modeling, where one can trade spatial
resolution and fidelity for computational performance depending on
the problem at hand. Between this two ends are the coarse-grained
(CG) models, where neighboring atoms are grouped and treated as
an individual interaction site or superatom. The CGmodels preserve
a reasonable level of details about molecular interactions while
dramatically improving computational performance in two crucial
ways: CG models have far fewer interactions (thus reducing the
cost per time step), and they involve moving heavier particles on
smoother potential energy surfaces (allowing a larger time step).
The computational efficiency of these CG models and their ability
to capture large-scale properties make simulations of membrane
assembly and vesicle fusion possible (Marrink et al., 2007; Orsi and
Essex, 2011; Risselada et al., 2008; Wu et al., 2011b).
