复杂环境下的自主变形虫导航
Self-assisted Amoeboid Navigation in Complex Environments
摘要：
背景资料：许多类型的活细胞需要响应外部刺激而移动，以完成其功能任务;这些任务包括从伤口愈合到免疫反应再到受精。虽然定向运动通常由外部信号决定，但实际的运动性也受到物理约束的限制，例如其他细胞和细胞外基质的存在。在障碍物存在下成功导航的能力不仅对生物体至关重要，而且可能在自主机器人运动的研究中被证明是相关的。方法/主要结论：我们研究了不同条件下的变形虫趋化导航的计算模型，从无障碍环境中的运动到障碍物之间的导航，最后到迷宫中的移动。我们使用迷宫作为一个简单的替身的运动任务与严重的限制，可以预期在密集的细胞外基质。尽管使用简单趋化性的代理可以成功地绕过小障碍，但大障碍的存在通常会导致代理捕获。我们进一步表明，采用一个简单的记忆机制，即分泌的排斥性化学物质的代理，帮助代理逃脱这样的陷阱。结论/意义：我们的主要结论是，采用简单趋化策略的细胞通常无法通过迷宫状几何形状，但简单的化学标记机制（我们称之为“自助”）显着提高了成功率。这一认识提供了重要的见解，可能会采用真正的细胞迁移在复杂的环境中，以及机器人导航策略的设计线索的机制。该结果可推广到更复杂的多细胞系统，并可用于哺乳动物细胞迁移和癌症转移的研究。
英文摘要：
Background: Living cells of many types need to move in response to external stimuli in order to accomplish their functional tasks; these tasks range from wound healing to immune response to fertilization. While the directional motion is typically dictated by an external signal, the actual motility is also restricted by physical constraints, such as the presence of other cells and the extracellular matrix. The ability to successfully navigate in the presence of obstacles is not only essential for organisms, but might prove relevant in the study of autonomous robotic motion. Methodology/principal findings: We study a computational model of amoeboid chemotactic navigation under differing conditions, from motion in an obstacle-free environment to navigation between obstacles and finally to moving in a maze. We use the maze as a simple stand-in for a motion task with severe constraints, as might be expected in dense extracellular matrix. Whereas agents using simple chemotaxis can successfully navigate around small obstacles, the presence of large barriers can often lead to agent trapping. We further show that employing a simple memory mechanism, namely secretion of a repulsive chemical by the agent, helps the agent escape from such trapping. Conclusions/significance: Our main conclusion is that cells employing simple chemotactic strategies will often be unable to navigate through maze-like geometries, but a simple chemical marker mechanism (which we refer to as "self-assistance") significantly improves success rates. This realization provides important insights into mechanisms that might be employed by real cells migrating in complex environments as well as clues for the design of robotic navigation strategies. The results can be extended to more complicated multi-cellular systems and can be used in the study of mammalian cell migration and cancer metastasis.
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