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Replication through cell division is one of the most fundamental processes of life and a major driver of dynamics in systems ranging from bacterial colonies to embryogenesis, tissues and tumors. While regulation often plays a role in shaping self-organization, mounting evidence suggests that many biologically relevant behaviors exploit principles based on a limited number of physical ingredients, and particle-based models have become a popular platform to reconstitute and investigate these emergent dynamics. However, incorporating division into such models often leads to aberrant mechanical fluctuations that hamper physically meaningful analysis. Here, we present a minimal model focusing on mechanical consistency during division. Cells are comprised of two nodes, overlapping disks which separate from each other during cell division, resulting in transient dumbbell shapes. Internal degrees of freedom, cell-cell interactions and equations of motion are designed to ensure force continuity at all times, including through division, both for the dividing cell itself as well as interaction partners, while retaining the freedom to define arbitrary anisotropic mobilities. As a benchmark, we also translate an established model of proliferating spherocylinders with similar dynamics into our theoretical framework. Numerical simulations of both models demonstrate force continuity of the new disk cell model and quantify our improvements. We also investigate some basic collective behaviors related to alignment and orientational order and find consistency both between the models and with the literature. A reference implementation of the model is freely available as a package in the Julia programming language based on $\mathit{InPartS}$. Our model is ideally suited for the investigation of mechanical observables such as velocities and stresses, and is easily extensible with additional features., Comment: 18 pages, 6 figures

Order and alignment are ubiquitous in growing colonies of rod-shaped bacteria due to the nematic properties of the constituent particles. These effects are the result of the active stresses generated by growth, passive mechanical interactions between cells, and flow-induced effects due to the shape of the confining container. However, how these contributing factors interact to give rise to the observed global alignment patterns remains elusive. Here, we study, in-silico, colonies of growing rod-shaped particles of different aspect ratios confined in channel-like geometries. A spatially resolved analysis of the stress tensor reveals a strong relationship between near-perfect alignment and an inversion of stress anisotropy for particles with large length-to-width ratios. We show that, in quantitative agreement with an asymptotic theory, strong alignment can lead to a decoupling of active and passive stresses parallel and perpendicular to the direction of growth, respectively. We demonstrate the robustness of these effects in a geometry that provides less restrictive confinement and introduces natural perturbations in alignment. Our results illustrate the complexity arising from the inherent coupling between nematic order and active stresses in growing active matter which is modulated by geometric and configurational constraints due to confinement., Comment: 9 pages, 5 figures

Flow-driven rotary motors drive functional processes in human society such as windmills and water wheels. Although examples of such rotary motors also feature prominently in cell biology, their synthetic construction at the nanoscale has thus far remained elusive. Here, we demonstrate flow-driven rotary motion of a self-organized DNA nanostructure that is docked onto a nanopore in a thin solid-state membrane. An elastic DNA bundle self assembles into a chiral conformation upon phoretic docking onto the solid-state nanopore, and subsequently displays a sustained unidirectional rotary motion of up to 20 revolutions/s. The rotors harness energy from a nanoscale water and ion flow that is generated by a static (electro)chemical potential gradient in the nanopore that is established through a salt gradient or applied voltage. These artificial nanoengines self-organize and operate autonomously in physiological conditions, paving a new direction in constructing energy-transducing motors at nanoscale interfaces.

We present a method for both cross estimation and iterated time series prediction of spatio temporal dynamics based on reconstructed local states, PCA dimension reduction, and local modelling using nearest neighbour methods. The effectiveness of this approach is shown for (noisy) data from a (cubic) Barkley model, the Bueno-Orovio-Cherry-Fenton model, and the Kuramoto-Sivashinsky model.