The study of C. elegans behavior as a biophysical science

作为生物物理科学的线虫行为研究

• 批准号: RGPIN-2019-04487
• 负责人: Ryu, William
• 金额: $ 1.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680042
• 关键词: study; C.; elegans; behavior; biophysical

We are studying “the Physics of Behavior” through interdisciplinary and quantitative studies of the model organism C. elegans. The biological sciences have developed powerful and sophisticated molecular tools to help us understand how biological components (genes, proteins, and cells) form the complex networks underlying behavior. However, the study of behavior itself has lagged far behind, and our lab is trying to address this gap. Through the use of novel behavioral measurement, instrumentation, and analysis, we strive to produce interpretable, widely applicable models of sensory behavior. In this pursuit we study the sensory behavior of the roundworm C. elegans, an ideal model for this type of work. It is a relatively simple organism (e.g. ~300 neurons, ~1000 cells), but it can perform a number of complex behaviors including searching, escaping, and associative learning in response to measurements it makes of its environment (temperature, chemicals, etc).*** Here I describe the evolution of one experiment to illustrate our interdisciplinary style and approach. To study the thermal “pain” response of C. elegans we developed a novel assay using an infra-red laser to programmatically apply a precise level of thermal heating to specific parts of C. elegans' body. Using this laser-based heating assay, we demonstrated the power of multi-parameter measurement on the analysis of thermal “pain” behavior of a candidate mutant strain library (Ghosh, 2012). We then improved this instrument to programmatically stimulate parts of the worm's body with tightly focused beams of infrared laser light, to determine the spatial receptive field of the worm's thermal pain response. Using multi-parameter quantification of the “pain” behavior we showed that: 1) C. elegans can discriminate thermal stimuli to a precision of at least 80 microns when focused on its mid-body, 2) the neuron PVD is the sensory neuron that is required for the mid-body response, and 3) a number of genes encoding glutamate receptors and TRPV-like channels are likely involved in this nociceptive transduction of heat (Mohammadi, 2013). We then mapped this high-dimensional behavioral data and their dynamics onto a lower dimensional space to allow for coarse grain modeling. Using this data, we then produced a quantitative, statistical model for thermal pain transduction, where the worm's level of pain is inferred by reading the “body language” of its behavioral response. Using this model we objectively determined changes in pain transduction due to perturbations of genes, neurons, or experience (Leung, 2016). On the same data set, we took a different approach and used an automated inference technique to generate a set of precise, predictive, and interpretable differential equations of this pain behavior (Daniels, 2018). This successful discovery of the dynamical system underlying the escape response illustrates how machine learning techniques can assist in the discovery of “equations of behavior.”**

我们正在通过对模式生物C的跨学科和定量研究来研究“行为物理学”。优雅的生物科学已经开发出强大而复杂的分子工具，帮助我们了解生物成分（基因、蛋白质和细胞）如何形成行为背后的复杂网络。然而，行为本身的研究已经远远落后，我们的实验室正在努力解决这一差距。通过使用新颖的行为测量，仪器和分析，我们努力产生可解释的，广泛适用的感官行为模型。在这一追求中，我们研究了蛔虫C的感官行为。elegans，一个理想的模型为这种类型的工作。它是一个相对简单的生物体（例如~300个神经元，~1000个细胞），但它可以执行许多复杂的行为，包括搜索，逃避和联想学习，以响应它对其环境（温度，化学物质等）的测量。在这里，我描述了一个实验的演变，以说明我们的跨学科的风格和方法。研究了C.我们开发了一种新的测定方法，使用红外激光以编程方式将精确水平的热加热应用于C.优雅的身体。使用这种基于激光的加热测定，我们证明了多参数测量对候选突变株文库的热“疼痛”行为分析的功效（Ghosh，2012）。然后，我们改进了这台仪器，用红外激光的紧密聚焦光束以编程方式刺激蠕虫身体的某些部分，以确定蠕虫热痛反应的空间感受野。采用多参数量化的“疼痛”行为，我们发现：1）C。当聚焦于其中间体时，秀丽线虫可以将热刺激区分到至少80微米的精度，2）神经元PVD是中间体反应所需的感觉神经元，以及3）编码谷氨酸受体和TRPV样通道的许多基因可能参与这种热的伤害性转导（Mohammadi，2013）。然后，我们将这些高维行为数据及其动态映射到一个低维空间，以允许粗粒度建模。利用这些数据，我们建立了一个热疼痛传导的定量统计模型，其中蠕虫的疼痛程度是通过阅读其行为反应的“肢体语言”来推断的。使用该模型，我们客观地确定了由于基因、神经元或经验的扰动而引起的疼痛转导的变化（Leung，2016）。在相同的数据集上，我们采用了不同的方法，并使用自动推理技术来生成一组精确的，预测的和可解释的疼痛行为微分方程（Daniels，2018）。对逃避反应背后的动力系统的成功发现说明了机器学习技术如何帮助发现“行为方程"。