A lipid vesicle exposed to an interior sucrose and an exterior glucose solution can attain a variety of multispherical shapes with different numbers of large and small spheres. For each shape, all spheres are connected by narrow membrane necks.

Reaction-diffusion systems encapsulated within giant unilamellar vesicles (GUVs) can lead to shape oscillations of these vesicles as recently observed for the bacterial Min protein system. This system contains two Min...

There are many excellent plotting libraries. Each excels at a specific use case: one is particularly suited for creating printable 2D figures for publication, another for generating interactive 3D graphics, while a third may have excellent LaTeX integration or be ideal for creating dashboards on the web. The aim of Plots.jl is to enable the user to use the same syntax to interact with a range of different plotting libraries, making it possible to change the library that does the actual plotting (the backend) without needing to touch the code that creates the content -and without having to learn multiple application programming interfaces (API). This is achieved by separating the specification of the plot from the implementation of the graphical backend. This plot specification is extendable by a recipe system that allows package authors and users to create new types of plots, as well as to specify how to plot any type of object (e.g. a statistical model, a map, a phylogenetic tree or the solution to a system of differential equations) without depending on the Plots.jl package. This design supports a modular ecosystem structure for plotting and yields a high code reuse potential across the entire Julia package ecosystem. Plots.jl is publicly available at https://github.com/JuliaPlots/Plots.jl.

Abstract.We study complex networks of stochastic two-state units. Our aim is to model discrete stochastic excitable dynamics with a rest and an excited state. Both states are assumed to possess different waiting time distributions. The rest state is treated as an activation process with an exponentially distributed life time, whereas the latter in the excited state shall have a constant mean which may originate from any distribution. The activation rate of any single unit is determined by its neighbors according to a random complex network structure. In order to treat this problem in an analytical way, we use a heterogeneous mean-field approximation yielding a set of equations generally valid for uncorrelated random networks. Based on this derivation we focus on random binary networks where the network is solely comprised of nodes with either of two degrees. The ratio between the two degrees is shown to be a crucial parameter. Dependent on the composition of the network the steady states show the usual transition from disorder to homogeneously ordered bistability as well as new scenarios that include inhomogeneous ordered and disordered bistability as well as tristability. The various steady states differ in their spiking activity expressed by a state dependent spiking rate. Numerical simulations agree with analytic results of the heterogeneous mean-field approximation.

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Processive enzymes, such as polymerases or ribosomes, are often studied in en-semble or bulk experiments through the monitoring of time-dependent signals, such as fluorescence time traces. However, ensemble signals are a superposition of the time traces of all molecules in the reaction and can exhibit less distinct features than individual single-molecule signals due to the stochasticity of biomolecular processes. Here, we demonstrate that under certain conditions, bulk signals from processive reactions can still be decomposed to reveal hidden information about individual reaction steps. Using mRNA translation as a case study for processive biochemical reactions, we explore the limits of least-squares approaches for ensemble fluorescence signal decomposition. Specifically, we show that decomposing a noisy ensemble signal generated by the translation of mRNAs containing more than a few codons represents an ill-posed problem, which can be addressed through Tikhonov regularization. Our findings can help to increase the information content extracted from bulk experiments, thereby expanding the range of these time- and cost-efficient methods.