Comparing individual-based models of collective cell motion in a benchmark flow geometry.

Collectively coordinated cell migration plays a role in tissue embryogenesis, cancer, homeostasis and healing. To study these processes, different cell-based modelling approaches have been developed, ranging from lattice-based cellular automata to lattice-free models that treat cells as point-like particles or extended detailed cell shape contours. In the spirit of what Osborne [, 2017, , 1-34] did for cellular tissue structure simulation models, we here compare five simulation models of collective cell migration, chosen to be representatives in increasing order of included detail.

Transport threshold in a quantum model for the KscA ion channel.

The mechanism behind the high throughput rate in Kchannels is still an open problem. However, recent simulations have shown that the passage of potassium through the Kchannel core, the so-called selectivity filter (SF), is water-free against models where the strength of Coulomb repulsion freezes ions conduction. Thus, it has been suggested that coherent quantum hopping might be relevant in mediating ion conduction.

A single active ring model with velocity self-alignment.

Cellular tissue behavior is a multiscale problem. At the cell level, out of equilibrium, biochemical reactions drive physical cell-cell interactions in a typical active matter process. Cell modeling computer simulations are a robust tool to explore countless possibilities and test hypotheses.

Coulomb interaction rules timescales in potassium ion channel tunneling.

Assuming the selectivity filter of KcsA potassium ion channel may exhibit quantum coherence, we extend a previous model by Vaziri and Plenio (2010 New J. Phys. 12 085001) to take into account Coulomb repulsion between potassium ions.

Mean-cluster approach indicates cell sorting time scales are determined by collective dynamics.

Cell migration is essential to cell segregation, playing a central role in tissue formation, wound healing, and tumor evolution. Considering random mixtures of two cell types, it is still not clear which cell characteristics define clustering time scales. The mass of diffusing clusters merging with one another is expected to grow as t^{d/d+2} when the diffusion constant scales with the inverse of the cluster mass.