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Search efficiency of biased migration towards stationary or moving targets in heterogeneously structured environments

机译:在异构地结构环境中搜索偏置或移动目标的偏见迁移的效率

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摘要

Efficient search acts as a strong selective force in biological systems ranging from cellular populations topredator-prey systems. The search processes commonly involve finding a stationary or mobile target within aheterogeneously structured environment where obstacles limit migration. An open generic question is whetherrandom or directionally biased motions or a combination of both provide an optimal search efficiency and howthat depends on the motility and density of targets and obstacles. To address this question, we develop a simplemodel that involves a random walker searching for its targets in a heterogeneous medium of bond percolationsquare lattice and used mean first passage time () as an indication of average search time. Our analysis revealsa dual effect of directional bias on the minimum value of . For a homogeneous medium, directionality alwaysdecreases and a pure directional migration (a ballistic motion) serves as the optimized strategy, while for aheterogeneous environment, we find that the optimized strategy involves a combination of directed and randommigrations. The relative contribution of these modes is determined by the density of obstacles and motility oftargets. Existence of randomness and motility of targets add to the efficiency of search. Our study reveals genericand simple rules that govern search efficiency. Our findings might find application in a number of areas includingimmunology, cell biology, ecology, and robotics.
机译:高效的搜索是从蜂窝群体的生物系统中的强烈选择性力量,从蜂窝群体填充器 - 猎物系统中。搜索过程通常涉及在Aheterogeally结构化环境中找到静止或移动目标,其中障碍限制迁移。开放的通用问题是是否违规或定向偏见的动作或两者的组合提供了最佳的搜索效率,而且依赖于目标和障碍物的运动和密度。为了解决这个问题,我们开发了一种SimpleModel,它涉及一个随机步行者在粘合覆盖晶格的异构介质中寻找其目标,并使用平均第一通道时间()作为平均搜索时间的指示。我们的分析显示了定向偏置的双向偏差对的最小值。对于均匀的介质,方向性始终分解和纯粹的方向迁移(弹道运动)用作优化的策略,而对于AheteroceOce环境,我们发现优化的策略涉及指导和兰特的组合。这些模式的相对贡献由障碍和运动的密度决定。目标的存在性和目标的动机增加了搜索效率。我们的研究揭示了管理搜索效率的通用和简单的规则。我们的研究结果可能会在包括Immunology,Cell Biology,Ecology和机器人的许多领域中申请。

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