AMS-SS: Multiscale stochastic model of myxobacteria dynamics

蜂拥而至，许多细胞的协调运动，促进它们在适当潮湿的固体介质表面的扩散。与集中细胞的聚集不同，蜂群将细胞分散在表面上。作为进化的结果，群体对能量的利用进行了优化，以便扩大并覆盖尽可能大的区域，使单个细胞能够最大限度地获得营养和氧气。尽管进行了多年的实验，但群体动力学的主要问题仍然悬而未决：单个细胞如何在对周围环境只有局部了解的情况下产生这种最佳的细胞排列？该研究小组将使用大量可用的关于细胞行为和细胞间相互作用的可靠实验数据作为构建块，以开发基于短期和远程相互作用的全面的数学和计算多维混合细菌聚集模型。更好地了解细菌的集体运动将有助于揭示细菌如何在疾病中感染人体组织。例如，囊性纤维化是一种病原菌，铜绿假单胞菌聚集在肺表面，堵塞患者的呼吸道。变形杆菌，另一种病原体，也是通过蜂群感染的。所提出的建模方法将通过研究黄色粘球菌的蜂群来验证。为了将不同尺度的描述(从数千到数百万个单元)结合在一起，将开发新的数学技术和工具，包括随机分析、非线性分析和动力学理论的元素。该项目的关键部分将是将建模结果与实验结果联系起来，并提出新的生物实验。该项目的一个重要组成部分是对研究生进行生物和数学方面的跨学科培训。

Swarming, the coordinated motion of many cells, facilitates their spread on the surface of a suitably moist solid medium. Unlike aggregation that concentrates cells, swarming disperses cells across a surface. As a result of evolution the use of energy is optimized by the swarm in order for it to expand and cover the largest possible area, allowing individual cells maximal access to nutrient and oxygen. Despite years of experiments the main problem of swarm dynamics is wide open: how is this optimal cell arrangement produced by individual cells with only local knowledge of their surroundings? The research group will use a large amount of available reliable experimental data on cell behavior and interactions between cells as building blocks for developing comprehensive mathematical and computational multi-dimensional hybrid models of bacterial swarming based on short and long range interactions. Better understanding of the collective motion of bacteria will shed light on how bacteria infect human tissues in disease. For example, in Cystic Fibrosis the pathogenic bacterium, Pseudomonas aeruginosa swarms over the surface of the lung, clogging the patient's airways. Proteus, another pathogen, also infects by swarming.The proposed modeling approach will be demonstrated by studying swarming of Myxococcus xanthus; which swarms rapidly, has been studied for decades, and whose numerous swarming mutants have been identified and characterized. To bring different scale levels of description together (from thousands to millions of cells), new mathematical techniques and tools will be developed including elements of stochastic analysis, nonlinear analysis and kinetic theory. The essential part of the project will be to connect results of modeling with experimental results and propose new biological experiments. An important component of the project will be the interdisciplinary training of graduate students in biology and mathematics.