Recently we developed a stochastic particle system describing local interactions between cyanobacteria. We focused on the common freshwater cyanobacteria Synechocystis sp., which are coccoidal bacteria that utilize group dynamics to move toward a light source, a motion referred to as phototaxis. We were particularly interested in the local interactions between cells that were located in low to medium density areas away from the front. The simulations of our stochastic particle system in 2D replicated many experimentally observed phenomena, such as the formation of aggregations and the quasi-random motion of cells. In this paper, we seek to develop a better understanding of group dynamics produced by this model. To facilitate this study, we replace the stochastic model with a system of ordinary differential equations describing the evolution of particles in 1D. Unlike many other models, our emphasis is on particles that selectively choose one of their neighbors as the preferred direction of motion. Furthermore, we incorporate memory by allowing persistence in the motion. We conduct numerical simulations which allow us to efficiently explore the space of parameters, in order to study the stability, size, and merging of aggregations.

最近我们开发了一个随机粒子系统来描述蓝藻之间的局部相互作用。我们专注于常见的淡水蓝藻集胞藻sp.，它们是利用群体动力学向光源移动的球状细菌，这种运动被称为趋光性。我们对位于远离前沿的中低密度区域的细胞之间的局部相互作用特别感兴趣。我们在 2D 中对随机粒子系统的模拟复制了许多实验观察到的现象，例如聚集体的形成和细胞的准随机运动。在本文中，我们试图更好地理解由该模型产生的群体动态。为了促进这项研究，我们用描述一维粒子演化的常微分方程系统替换了随机模型。与许多其他模型不同，我们的重点是选择性地选择其邻居之一作为首选运动方向的粒子。此外，我们通过允许运动中的持久性来合并记忆。我们进行数值模拟，使我们能够有效地探索参数空间，以研究聚合的​​稳定性、大小和合并。