Shape fluctuations and elastic properties of two-component bilayer membranes

The elastic properties of two-component bilayer membranes are studied using a coarse grain model for amphiphilic molecules. The two species of amphiphiles considered here differ only in their length. Molecular Dynamics simulations are performed in order to analyze the shape fluctuations of the two-component bilayer membranes and to determine their bending rigidity. Both the bending rigidity and its inverse are found to be nonmonotonic functions of the mole fraction $x_{\rm B}$ of the shorter B-amphiphiles and, thus, do not satisfy a simple lever rule. The intrinsic area of the bilayer also exhibits a nonmonotonic dependence on $x_{\rm B}$ and a maximum close to $x_{\rm B} \simeq 1/2$.Comment: To appear on Europhysics Letter

Role of Bilayer Tilt Difference in Equlibrium Membrane Shapes

Lipid bilayer membranes below their main transition have two tilt order parameters, corresponding to the two monolayers. These two tilts may be strongly coupled to membrane shape but only weakly coupled to each other.We discuss some implications of this observation for rippled and saddle phases, spontaneous breaking bilayer tubules, and bicontinuous phases. Tilt difference introduces a lengthscale into the elastic theory of tilted fluid membranes. It can drive an instability of the flat phase; it also provides a simple spontaneous breaking of inversion symmetry seen in some recent experiments

Computational Approaches to Explore Bacterial Toxin Entry into the Host Cell

Many bacteria secrete toxic protein complexes that modify and disrupt essential processes in the infected cell that can lead to cell death. To conduct their action, these toxins often need to cross the cell membrane and reach a specific substrate inside the cell. The investigation of these protein complexes is essential not only for understanding their biological functions but also for the rational design of targeted drug delivery vehicles that must navigate across the cell membrane to deliver their therapeutic payload. Despite the immense advances in experimental techniques, the investigations of the toxin entry mechanism have remained challenging. Computer simulations are robust complementary tools that allow for the exploration of biological processes in exceptional detail. In this review, we first highlight the strength of computational methods, with a special focus on all-atom molecular dynamics, coarse-grained, and mesoscopic models, for exploring different stages of the toxin protein entry mechanism. We then summarize recent developments that are significantly advancing our understanding, notably of the glycolipid–lectin (GL-Lect) endocytosis of bacterial Shiga and cholera toxins. The methods discussed here are also applicable to the design of membrane-penetrating nanoparticles and the study of the phenomenon of protein phase separation at the surface of the membrane. Finally, we discuss other likely routes for future development

Negative poisson ratio in two-dimensional networks under tension

The elastic properties of two-dimensional networks under tension are studied by the mean-field approximation and Monte Carlo simulation. The networks are characterized by fixed (polymerized) connectivity and either a square-well or a Hooke’s-law interaction among their components. Both self-avoiding and phantom networks are examined. The elastic properties of Hooke’s-law networks at large equilibrium length are found to be well represented by a mean-field model. All the networks investigated show a negative Poisson ratio over a range of tension. At finite tension, the phantom networks exhibit a phase transition to a collapsed state

Reconstructing the brain: from image stacks to neuron synthesis

Large-scale brain initiatives such as the US BRAIN initiative and the European Human Brain Project aim to marshall a vast amount of data and tools for the purpose of furthering our understanding of brains. Fundamental to this goal is that neuronal morphologies must be seamlessly reconstructed and aggregated on scales up to the whole rodent brain. The experimental labor needed to manually produce this number of digital morphologies is prohibitively large. The BigNeuron initiative is assembling community-generated, open-source, automated reconstruction algorithms into an open platform, and is beginning to generate an increasing flow of high quality reconstructed neurons. We propose a novel extension of this workflow to use this data stream to generate an unlimited number of statistically equivalent, yet distinct, digital morphologies. This will bring automated processing of reconstructed cells into digital neurons to the wider neuroscience community, and enable a range of morphologically- accurate computational models

Escape from a metastable well under a time-ramped force

Thermally activated escape of an over-damped particle from a metastable well under the action of a time-ramped force is studied. We express the mean first passage time (MFPT) as the solution to a partial differential equation, which we solve numerically for a model case. We discuss two approximations of the MFPT, one of which works remarkably well over a wide range of loading rates, while the second is easy to calculate and can provide a valuable first estimate.Comment: 9 pages, including 2 figure

The Role of Bilayer Tilt Difference in Equilibrium Membrane Shapes

Lipid bilayer membranes below their main transition have two tilt order parameters, corresponding to the two monolayers. These two tilts may be strongly coupled to membrane shape but only weakly coupled to each other. We discuss some implications of this observation for rippled and saddle phases, bilayer tubules, and bicontinuous phases. Tilt difference introduces a length scale into the elastic theory of tilted fluid membranes. It can drive an instability of the flat phase; it also provides a simple mechanism for the spontaneous breaking of inversion symmetry seen in some recent experiments.Comment: Latex file; .ps available at http://dept.physics.upenn.edu/~nelson/saddle.p

A Topological Representation of Branching Neuronal Morphologies

The online version of this article (https://doi.org/10.1007/s12021-017-9341-1) contains supplementary material, which is available to authorized users. Among others, we thank Athanassia Chalimourda and Katherine Turner for helpful conversations in various stages of this research and Jay Coggan for a critical reading of the manuscript. We also thank Hanchuan Peng and Xiaoxiao Liu for providing and curating the BigNeuron datasets. This work was supported by funding for the Blue Brain Project (BBP) from the ETH Domain. P.D. and R.L. were supported part by the Blue Brain Project and by the start-up grant of KH. Partial support for P.D. has been provided by the Advanced Grant of the European Research Council GUDHI (Geometric Understanding in Higher Dimensions). MS was supported by the SNF NCCR “Synapsy”.Peer reviewedPublisher PD