A key question in receptor signaling is how specificity is realized, particularly when different receptors trigger the same biochemical pathway(s). A notable case is the two β‐adrenergic receptor (β‐AR) subtypes, β1 and β2, in cardiomyocytes. They are both coupled to stimulatory Gs proteins, mediate an increase in cyclic adenosine monophosphate (cAMP), and stimulate cardiac contractility; however, other effects, such as changes in gene transcription leading to cardiac hypertrophy, are prominent only for β1‐AR but not for β2-AR. Here, we employ highly sensitive fluorescence spectroscopy approaches, in combination with a fluorescent β‐AR antagonist, to determine the presence and dynamics of the endogenous receptors on the outer plasma membrane as well as on the T-tubular network of intact adult cardiomyocytes. These techniques allow us to visualize that the β2‐AR is confined to and diffuses within the T-tubular network, as opposed to the β1‐AR, which is found to diffuse both on the outer plasma membrane as well as on the T-tubules. Upon overexpression of the β2‐AR, this compartmentalization is lost, and the receptors are also seen on the cell surface. Such receptor segregation depends on the development of the T-tubular network in adult cardiomyocytes since both the cardiomyoblast cell line H9c2 and the cardiomyocyte-differentiated human-induced pluripotent stem cells express the β2‐AR on the outer plasma membrane. These data support the notion that specific cell surface targeting of receptor subtypes can be the basis for distinct signaling and functional effects.
We combine parallelization and cluster Monte Carlo for hard sphere systems and present a parallelized event chain algorithm for the hard disk system in two dimensions. For parallelization we use a spatial partitioning approach into simulation cells. We find that it is crucial for correctness to ensure detailed balance on the level of Monte Carlo sweeps by drawing the starting sphere of event chains within each simulation cell with replacement. We analyze the performance gains for the parallelized event chain and find a criterion for an optimal degree of parallelization. Because of the cluster nature of event chain moves massive parallelization will not be optimal. Finally, we discuss first applications of the event chain algorithm to dense polymer systems, i.e., bundle-forming solutions of attractive semiflexible polymers.
We study the adsorption of semiflexible polymers such as polyelectrolytes or DNA on planar and curved substrates, e.g., spheres or washboard substrates via short-range potentials using extensive Monte Carlo simulations, scaling arguments, and analytical transfer matrix techniques. We show that the adsorption threshold of stiff or semiflexible polymers on a planar substrate can be controlled by polymer stiffness: adsorption requires the highest potential strength if the persistence length of the polymer matches the range of the adsorption potential. On curved substrates, i.e., an adsorbing sphere or an adsorbing washboard surface, the adsorption can be additionally controlled by the curvature of the surface structure. The additional bending energy in the adsorbed state leads to an increase of the critical adsorption strength, which depends on the curvature radii of the substrate structure. For an adsorbing sphere, this gives rise to an optimal polymer stiffness for adsorption, i.e., a local minimum in the critical potential strength for adsorption, which can be controlled by curvature. For two- and three-dimensional washboard substrates, we identify the range of persistence lengths and the mechanisms for an effective control of the adsorption threshold by the substrate curvature.
We propose an efficient Monte Carlo algorithm for the off-lattice simulation of dense hard sphere polymer melts using cluster moves, called event chains, which allow for a rejection-free treatment of the excluded volume. Event chains also allow for an efficient preparation of initial configurations in polymer melts. We parallelize the event chain Monte Carlo algorithm to further increase simulation speeds and suggest additional local topology-changing moves ("swap" moves) to accelerate equilibration. By comparison with other Monte Carlo and molecular dynamics simulations, we verify that the event chain algorithm reproduces the correct equilibrium behavior of polymer chains in the melt. By comparing intrapolymer diffusion time scales, we show that event chain Monte Carlo algorithms can achieve simulation speeds comparable to optimized molecular dynamics simulations. The event chain Monte Carlo algorithm exhibits Rouse dynamics on short time scales. In the absence of swap moves, we find reptation dynamics on intermediate time scales for long chains.
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