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Modeling of chemotactic steering of bacteria-based microrobot using a population-scale approach

机译:基于种群规模方法的基于细菌的微型机器人趋化操纵建模

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摘要

The bacteria-based microrobot (Bacteriobot) is one of the most effective vehicles for drug delivery systems. The bacteriobot consists of a microbead containing therapeutic drugs and bacteria as a sensor and an actuator that can target and guide the bacteriobot to its destination. Many researchers are developing bacteria-based microrobots and establishing the model. In spite of these efforts, a motility model for bacteriobots steered by chemotaxis remains elusive. Because bacterial movement is random and should be described using a stochastic model, bacterial response to the chemo-attractant is difficult to anticipate. In this research, we used a population-scale approach to overcome the main obstacle to the stochastic motion of single bacterium. Also known as Keller-Segel's equation in chemotaxis research, the population-scale approach is not new. It is a well-designed model derived from transport theory and adaptable to any chemotaxis experiment. In addition, we have considered the self-propelled Brownian motion of the bacteriobot in order to represent its stochastic properties. From this perspective, we have proposed a new numerical modelling method combining chemotaxis and Brownian motion to create a bacteriobot model steered by chemotaxis. To obtain modeling parameters, we executed motility analyses of microbeads and bacteriobots without chemotactic steering as well as chemotactic steering analysis of the bacteriobots. The resulting proposed model shows sound agreement with experimental data with a confidence level <0.01.
机译:基于细菌的微型机器人(Bacteriobot)是用于药物输送系统的最有效的载体之一。细菌机器人由含有治疗药物和细菌的微珠作为传感器和致动器组成,该微珠可以靶向并引导细菌机器人到达其目的地。许多研究人员正在开发基于细菌的微型机器人并建立模型。尽管做出了这些努力,但通过趋化性操纵的细菌机器人的运动模型仍然难以捉摸。由于细菌运动是随机的,应该使用随机模型来描述,因此很难预测细菌对化学吸引剂的反应。在这项研究中,我们使用了人口规模的方法来克服单一细菌随机运动的主要障碍。在趋化性研究中也称为Keller-Segel方程,人口规模方法并不新鲜。这是一个精心设计的模型,源于运输理论,适用于任何趋化性实验。另外,我们已经考虑了细菌机器人的自推进布朗运动,以表示其随机特性。从这个角度出发,我们提出了一种将趋化性和布朗运动相结合的新数值建模方法,以创建一个由趋化性操纵的细菌机器人模型。为了获得建模参数,我们对没有化学趋向操纵的微珠和细菌机器人进行了运动分析,并对细菌机器人进行了趋化操纵分析。最终提出的模型显示出与实验数据的合理一致性,置信度<0.01。

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