Density Fluctuations in Stochastic Kinematic Flows

Worsfold, Jeremy; Rogers, Tim; Milewski, Paul A.

doi:10.1137/22m1494166

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…The porous-medium equation with m = 2 can also be derived from microscopic movement rules when one takes into account volume exclusion [37][38][39], starting from on-lattice [40,41] and also from off-lattice agent-based models [11,[42][43][44]. Further, the case with m = 3 can be identified as the mean-field limit of a system of interacting agents with a particular diffusive scaling [45] and has also been suggested as the simplest model to relate the dispersal velocity to both the density and its gradient [46].…”

Section: Simple Models Of Tissue Growthmentioning

confidence: 99%

Quantifying tissue growth, shape and collision via continuum models and Bayesian inference

Falcó

Cohen

Carrillo

et al. 2023

J. R. Soc. Interface.

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Although tissues are usually studied in isolation, this situation rarely occurs in biology, as cells, tissues and organs coexist and interact across scales to determine both shape and function. Here, we take a quantitative approach combining data from recent experiments, mathematical modelling and Bayesian parameter inference, to describe the self-assembly of multiple epithelial sheets by growth and collision. We use two simple and well-studied continuum models, where cells move either randomly or following population pressure gradients. After suitable calibration, both models prove to be practically identifiable, and can reproduce the main features of single tissue expansions. However, our findings reveal that whenever tissue–tissue interactions become relevant, the random motion assumption can lead to unrealistic behaviour. Under this setting, a model accounting for population pressure from different cell populations is more appropriate and shows a better agreement with experimental measurements. Finally, we discuss how tissue shape and pressure affect multi-tissue collisions. Our work thus provides a systematic approach to quantify and predict complex tissue configurations with applications in the design of tissue composites and more generally in tissue engineering.

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Section: Simple Models Of Tissue Growthmentioning

confidence: 99%

Quantifying tissue growth, shape and collision via continuum models and Bayesian inference

Falcó

Cohen

Carrillo

et al. 2023

J. R. Soc. Interface.

View full text Add to dashboard Cite

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Collective synchronization through noise cancellation

Worsfold,

Rogers

2024

Phys. Rev. E

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After decades of study, there are only two known mechanisms to induce global synchronization in a population of oscillators: Deterministic coupling and common forcing. The inclusion of independent noise in these models typically serves to drive disorder, increasing the stability of the incoherent state. Here we show that the reverse is also possible. We propose and analyze a simple general model of purely noise coupled oscillators. In the first explicit choice of noise coupling, we find the linear response around incoherence is identical to that of the paradigmatic Kuramoto model but exhibits binary phase locking instead of full coherence. We characterize the phase diagram, stationary states, and approximate low-dimensional dynamics for the model, revealing the curious behavior of this mechanism of synchronization. In the second minimal case we connect the final synchronized state to the initial conditions of the system. Published by the American Physical Society 2024

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Stay in your lane: Density fluctuations in multi-lane traffic

Worsfold,

Rogers

2024

EPL

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When a new vehicle joins a lane, those behind may have to temporarily slow to accommodate them. Changing lane can be forced due to lane drops or junctions, but may also take place spontaneously at discretion of drivers, and recent studies have found that traffic jams and traffic oscillations can form even without such bottlenecks.Understanding how lane changing behaviour affects traffic flow is important for learning how to design roads and control traffic more effectively.Here, we present a stochastic model of spontaneous lane changing which exhibits a reduction in the overall flow of traffic. By examining the average flow rate both analytically and through simulations we find a definitive slow down of vehicles due to random switching between lanes. This results in the fundamental diagram depending on the rate of lane switching.By extending the model to three lane traffic we find a larger impact on the flow of the middle lane compared to the side lanes.

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Density Fluctuations in Stochastic Kinematic Flows

Cited by 3 publications

References 37 publications

Quantifying tissue growth, shape and collision via continuum models and Bayesian inference

Quantifying tissue growth, shape and collision via continuum models and Bayesian inference

Collective synchronization through noise cancellation

Stay in your lane: Density fluctuations in multi-lane traffic

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