COLLAB: From Structure to Information in Mechanosensory Systems. The role of Sensor Morphology in Detecting Fluid Signals.

摘要-场与江命数：0718832-合作研究：从机械感觉系统中的结构到信息。传感器形态在探测流体信号中的作用。桡足类呈现出惊人的多样性，触角和绒毛的形态、方向和装饰程度。这种多样性的原因和后果尚不清楚，但形态变异的惊人程度表明，机械传感器特性和它们的感觉作用之间存在结构-功能关系。本研究以桡足类为模型系统，研究非稳态流体条件下机械传感结构的形状与其运动之间的关系。浮游桡足动物为机械接收提供了一个独特的模型系统，因为：1)桡足类对流体信号表现出各种行为反应；2)桡足类机械传感系统的基本特性很容易识别，并可能在不同的物种中保存；3)由于它们的大小，桡足类在低雷诺数下工作，此时流体运动保持相对连贯，易于量化和建模。此外，桡足类的一个真正独特的特征是它们极快的反应时间(毫秒)。生活在低雷诺数下，机械刺激会被粘性阻尼迅速减弱，导致流体速度(由稳定的游泳驱动)随着距离的平方而降低(Fields和Yen 2002)。因此，桡足类通常不会发现其他个体(包括捕食者)，直到它们彼此在几个身体长度内。由于距离很近，而且它们有快速移动的能力，因此需要毫秒的行为潜伏期。在如此短的潜伏期内收集足够的信息对桡足类动物来说是一个非同寻常的挑战，需要快速的发射速度和来自其天线的大量信号的空间整合。要确定流体运动、尾巴运动、神经放电速度和行为之间的关系，必须同时知道所有组件的动力学。使用单极或偶极运动的解析解，以前的研究已经计算出在稳态条件下传感器的流体特性应该是什么。然而，产生流体信号的机械装置和响应信号的传感器需要100-1000毫秒才能达到稳定状态。然而，正是在这关键的最初几毫秒内，桡足类(或任何其他行为潜伏期较短的生物)收集了相关信息。因此，稳态解决方案适用于在长时间内整合信息的生物体，但不适用于行为潜伏期较短的生物，如桡足类。量化流体运动的数值处理方法是可用的，但很少被应用。第二个考虑是如何对受体进行建模。主要由于分析上的易操纵性，大多数当前的Setal运动模型将头发描述为直径均匀的刚性圆柱体，并依赖于整个头发上的空间均匀稳定流动来移动它。然而，不同的刚毛具有不同的解剖特征，如角质层厚度不均匀、横截面直径不对称、刚毛弯曲和毛发细小的毛状突起，可能会影响流体运动向刚毛弯曲的传导。此外，诸如弧度之类的机械特性可能会引起角偏转与流体速度之间的关系发生很大变化。对于浸泡在水中的刚毛，而不是空气(许多模型已经应用在空气中)，这些影响可能更加明显。这个项目的目标是量化流体运动和感官形态之间的关系。对三种桡足类动物的不同刚毛的大小和宽度进行了扫描电子显微镜测量，并对角质层厚度和树突沿单个机械感受器轴部的穿透程度进行了透射电子显微镜测量。弯曲刚毛所需的力和个体毛发对实验室中描述良好的流动的生理反应将根据受体的物理特征进行量化。这些经验数据将被用来建立两个相互作用的模型：有限元方法和计算流体动力学(CFD)。有限元将被用来模拟已知形状和弯曲特性的单个刚毛的运动，而CFD将被用来计算施加在刚毛每个区域的水动力和扭矩以及刚毛对周围流动的影响。这些数据是理解这些神经学上简单的小生物体如何区别于无数生物和物理诱导的液体运动的基础。

ABSTRACT - Fields and JiangProposal Number: 0718832 - Collaborative Research: From structure to information in mechanosensory systems. The role of sensor morphology in detecting fluid signals.Copepods present a spectacular diversity of antennule and setal morphologies, orientations and degree of ornamentation. The causes and consequences of this diversity remain unexplored, but the staggering degree of morphological variation suggests structure-function relationships between mechanosensor properties and their sensory roles. Using copepods as a model system, this work will address the relationship between the shape of mechanosensory structures and their movement under non steady-state fluid conditions. Planktonic copepods provide a unique model system for mechanoreception because: 1) copepods show a variety of behavioral responses to fluid signals; 2) the basic properties of copepod's mechanosensory systems are easy to identify and are likely conserved across a diverse range of species; 3) because of their size, copepods operate at low Reynolds numbers where fluid motion remains comparatively coherent and easy to quantify and model. Also, one of the truly unique characteristics of copepods is their extremely rapid response times (milliseconds). Living at low Reynolds numbers, mechanical stimuli are attenuated quickly by viscous dampening causing fluid velocity (driven by steady swimming) to decrease with distance squared (Fields and Yen 2002). Consequently, copepods often do not detect other individuals (including predators) until they are within a few body lengths of each other. Because of the close proximity and their ability for rapid movement, behavioral latencies of milliseconds are required. Collecting enough information within this short latency period is an extraordinary challenge for the copepod and requires rapid firing rates and the spatial integration of numerous signals from their antennae.To determine the relationship between fluid motion, setal motion, nerve firing rates and behavior, the dynamics of all components must be simultaneously known. Using analytical solutions for monopole or dipole movement, previous studies have calculated what the fluid characteristics should be at the sensor under steady state conditions. However, the mechanical devises generating the fluid signals and the sensor responding to the signal require 100 - 1000 ms to reach steady state. Yet it is during these crucial first few milliseconds that copepods (or any other organism with short behavioral latency) gather the pertinent information. Therefore steady state solutions are appropriate for organisms that integrate information over long time periods but are not applicable for organisms such as copepods with short behavioral latencies. Numerical treatments for quantifying fluid motion are available but rarely applied. A second consideration is how the receptors are modeled. Due primarily to analytical tractability, most current models of setal motion characterize the hair as a rigid cylinder of uniform diameter and rely on spatially homogeneous-steady state flow over the entire hair to move it. However, anatomical features, such as non-uniform cuticular thickness, asymmetry in cross-sectional diameter, setal geniculations and fine hair-like projections from the setae differ between seta and likely affect the transduction of fluid motion to setal bending. Furthermore, mechanical features such as the degree of setal arcing may give rise to large changes in the relationship between angular deflection and fluid velocity. For setae immersed in water, as opposed to air (where many of the models have been applied) these effects may be even more pronounced.The goal of this project is to quantify the relationship between fluid motion and sensory morphology. SEM measurements of the size and width of different setae and TEM measurements of cuticular thickness and the extent of the dendritic penetration up the shaft of individual mechanoreceptors will be made for three species of copepods. The force required to bend the seta and the physiological response of individual hairs to the well described flows created in the lab will be quantified with respect to the physical characteristics of the receptor. The empirical data will be used to build two interacting models: Finite Element Method (FEM) and Computational Fluid Dynamics (CFD). The FEM will be used to model the motion of individual seta with known morphology and bending characteristics while the CFD will be used to calculate the hydrodynamic force and torque applied to each region of the seta and the influence of the seta on the surrounding flow. These data are fundamental to understanding how these small, neurologically simple organisms can distinguish from the myriad of biologically and physically induced fluid movements.