Motility Controlled Bacteria Transport in Porous Media
In this study, an attempt to extract the run and tumble motility of flagellated Azotobacter Vinelandii based on microscopic images of a large number of cells is presented. It is assumed that the bacterial cells can change direction during both runs and tumbles as observed from the movement trajectories. An unsupervised cluster analysis has been performed to fractionate each bacterial trajectory into run and tumble states, and then the distribution of parameters for each mode has been extracted by fitting mathematical distributions best representing the data. A Gaussian copula was used to model the autocorrelation in swimming velocity. For both run and tumble modes, Gamma distribution fits the marginal velocity best and Logistic distribution fits the deviation angle best. For the runtime distribution, log-logistic distribution and log-normal distribution, respectively, were found to do a better job than the traditionally agreed exponential distribution. A model was then developed to mimic the motility behavior of bacteria at the presence of flow. The model was applied to evaluate its ability to describe observed patterns of bacterial deposition on surfaces in a micro-model experiment with an approach velocity of 200 𝜇𝑚/𝑠. It was found that the model can qualitatively reproduce the attachment results of the micro-model setting.
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