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.

中文翻译：

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流体动力学相互作用在细菌种群趋化中的作用。

细菌如何感测局部化学梯度并决定移动是最近研究的一个有趣的领域。传统上已经使用描述单个细菌以速度跳跃过程运动的基于个体的模型或描述细菌密度演变的宏观PDE模型对细菌种群的趋化性进行建模。在这些模型中，细菌之间的水动力相互作用通常被忽略。然而，流体动力相互作用已显示出引起集体细菌运动和自组织的能力，从而导致较大的中尺度结构。在本文中，通过扩展混合计算模型来研究流体动力学相互作用在细菌趋化性中的作用，该模型包含了流体动力学相互作用并添加了经典速度跳跃模型的组成部分。结果表明，通过包括流体动力学相互作用，低初始体积分数的悬浮液可以在细菌聚集体中局部显示高浓度。此外，还表明，水动力相互作用增强了小团聚体与大团聚体的融合，并导致了与纯趋化性模型相比可能发生质变的团聚体行为。即，这些涌现簇的形状，数量和动力学上的差异。