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-2020-07123
• 负责人: Ryu, William
• 金额: $ 2.48万
• 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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741610
• 关键词: study; C.; elegans; behavior; biophysical

We study "the Physics of Behavior" through interdisciplinary and quantitative research of the model organism C. elegans. Biological sciences have developed powerful 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 phenotyping, instrumentation, and analysis, we strive to produce interpretable, widely applicable models of sensory behavior. In this pursuit we study the sensory behavior of the roundword 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. Here I describe an example to illustrate our integrated and interdisciplinary 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 phenotyping on analysis of thermal "pain" behavior of a candidate mutant strain library (Ghosh, 2012). 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 graining. 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 automated inference techniques 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." This proposal details our drive to understand behavior universally through quantitative, predictive, and interpretable models, using careful measurements and descriptions of behavior while integrating a wide range of interdisciplinary techniques drawn from genetics, molecular biology, and neuroscience.

我们通过对模式生物秀丽隐杆线虫的跨学科和定量研究来研究“行为的物理学”。生物科学已经开发出强大的分子工具来帮助我们理解生物成分（基因、蛋白质和细胞）是如何形成行为背后的复杂网络的。然而，对行为本身的研究远远落后，我们的实验室正在努力解决这一差距。通过使用新的行为表型，仪器和分析，我们努力产生可解释的，广泛适用的感觉行为模型。在这一追求中，我们研究了线虫的感觉行为，它是这类工作的理想模型。它是一种相对简单的生物（例如约300个神经元，约1000个细胞），但它可以执行许多复杂的行为，包括搜索、逃跑和联想学习，以响应它对环境的测量。在这里，我用一个例子来说明我们的综合和跨学科的方法。为了研究秀丽隐杆线虫的热“痛”反应，我们开发了一种新的实验方法，使用红外激光对秀丽隐杆线虫身体的特定部位进行精确的热加热。利用这种基于激光的加热实验，我们证明了多参数表型在候选突变菌株库的热“疼痛”行为分析中的作用（Ghosh, 2012）。通过对“疼痛”行为的多参数量化，我们发现：1)秀丽隐杆线虫可以识别热刺激，当聚焦在其中体时，其精度至少为80微米；2)神经元PVD是中体反应所需的感觉神经元；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较低维空间，以允许粗粒度化。利用这些数据，我们制作了一个热痛转导的定量统计模型，其中蠕虫的疼痛水平是通过阅读其行为反应的“肢体语言”来推断的。使用这个模型，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、可预测的、可解释的疼痛行为微分方程（Daniels, 2018）。这一成功发现了逃逸反应背后的动力系统，说明了机器学习技术如何帮助发现“行为方程”。本提案详细介绍了我们通过定量、预测和可解释的模型来理解普遍行为的动力，使用仔细的测量和描述行为，同时整合了来自遗传学、分子生物学和神经科学的广泛跨学科技术。