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Characterization of Cooperators in Quorum Sensing with 2D Molecular Signal Analysis
IEEE Transactions on Communications ( IF 7.2 ) Pub Date : 2020-01-01 , DOI: 10.1109/tcomm.2020.3036674
Yuting Fang , Adam Noel , Andrew W. Eckford , Nan Yang , Jing Guo

In quorum sensing (QS), bacteria exchange molecular signals to work together. An analytically-tractable model is presented for characterizing QS signal propagation within a population of bacteria and the number of responsive cooperative bacteria (i.e., cooperators) in a two-dimensional (2D) environment. Unlike prior works with a deterministic topology and a simplified molecular propagation channel, this work considers continuous emission, diffusion, degradation, and reception among randomly-distributed bacteria. Using stochastic geometry, the 2D channel response and the corresponding probability of cooperation at a bacterium are derived. Based on this probability, new expressions are derived for the moment generating function and different orders of moments of the number of cooperators. The analytical results agree with the simulation results obtained by a particle-based method. In addition, the Poisson and Gaussian distributions are compared to approximate the distribution of the number of cooperators and the Poisson distribution provides the best overall approximation. The derived channel response can be generally applied to any molecular communication model where single or multiple transmitters continuously release molecules into a 2D environment. The derived statistics of the number of cooperators can be used to predict and control the QS process, e.g., predicting and decreasing the likelihood of biofilm formation.

中文翻译:

使用 2D 分子信号分析表征群体感应中的合作者

在群体感应 (QS) 中,细菌交换分子信号以协同工作。提出了一种易于分析的模型,用于表征细菌种群内的 QS 信号传播和二维 (2D) 环境中响应性合作细菌(即合作者)的数量。与具有确定性拓扑和简化分子传播通道的先前工作不同,这项工作考虑了随机分布的细菌之间的连续发射、扩散、降解和接收。使用随机几何,可以推导出 2D 通道响应和细菌的相应合作概率。根据这个概率,推导出矩生成函数和合作者数的不同阶矩的新表达式。分析结果与基于粒子的方法获得的模拟结果一致。此外,将泊松分布和高斯分布进行比较以近似合作者数量的分布,而泊松分布提供了最佳的总体近似。导出的通道响应通常可以应用于任何分子通信模型,其中单个或多个发射器连续将分子释放到 2D 环境中。合作者数量的衍生统计可用于预测和控制QS过程,例如预测和降低生物膜形成的可能性。比较 Poisson 和 Gaussian 分布以近似估计合作者数量的分布,而 Poisson 分布提供了最佳的总体近似值。导出的通道响应通常可以应用于任何分子通信模型,其中单个或多个发射器连续将分子释放到 2D 环境中。合作者数量的衍生统计可用于预测和控制QS过程,例如预测和降低生物膜形成的可能性。比较 Poisson 和 Gaussian 分布以近似估计合作者数量的分布,而 Poisson 分布提供了最佳的总体近似值。导出的通道响应通常可以应用于任何分子通信模型,其中单个或多个发射器连续将分子释放到 2D 环境中。合作者数量的衍生统计可用于预测和控制QS过程,例如预测和降低生物膜形成的可能性。
更新日期:2020-01-01
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