Department of Mathematics and Statistics, Cleveland State University, 2121 Euclid Ave. Cleveland, OH 44115, United States of America.
Phys Biol. 2019 Dec 3;17(1):016003. doi: 10.1088/1478-3975/ab57af.
How bacteria sense local chemical gradients and decide to move has been a fascinating area of recent study. Chemotaxis of bacterial populations has been traditionally modeled using either individual-based models describing the motion of a single bacterium as a velocity jump process, or macroscopic PDE models that describe the evolution of the bacterial density. In these models, the hydrodynamic interaction between the bacteria is usually ignored. However, hydrodynamic interaction has been shown to induce collective bacterial motion and self-organization resulting in larger mesoscale structures. In this paper, the role of hydrodynamic interactions in bacterial chemotaxis is investigated by extending a hybrid computational model that incorporates hydrodynamic interactions and adding components from a classical velocity jump model. It is shown that by including hydrodynamic interactions, a suspension with a low initial volume fraction can exhibit locally high concentrations in bacterial aggregates. Also, it is shown that hydrodynamic interactions enhance the merging of the small aggregates into larger ones and lead to qualitatively different aggregate behavior than possible with pure chemotaxis models. Namely, differences in the shape, number, and dynamics of these emergent clusters.
细菌如何感知局部化学梯度并决定移动，这是最近研究的一个迷人领域。细菌群体的趋化性传统上是使用基于个体的模型来模拟的，这些模型将单个细菌的运动描述为速度跳跃过程，或者使用描述细菌密度演变的宏观 PDE 模型来模拟。在这些模型中，细菌之间的流体动力学相互作用通常被忽略。然而，流体动力学相互作用已被证明会引起细菌的集体运动和自组织，从而产生更大的介观结构。本文通过扩展一种混合计算模型来研究细菌趋化性中的流体动力学相互作用，该模型结合了流体动力学相互作用，并添加了经典速度跳跃模型的组件。结果表明，通过包括流体动力学相互作用，初始体积分数较低的悬浮液可以在细菌聚集体中表现出局部高浓度。此外，结果表明，流体动力学相互作用增强了小聚集体的合并，并导致与纯趋化性模型不同的聚集体行为。即，这些新兴簇的形状、数量和动力学的差异。
