Deciphering the dynamics of physiological systems using quantitative and computational approaches

使用定量和计算方法解读生理系统的动力学

• 批准号: RGPIN-2019-04520
• 负责人: Khadra, Anmar
• 金额: $ 3.06万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714980
• 关键词: Deciphering; dynamics; physiological; systems; using

Physiological systems exhibit varying degrees of complexity and fascinating ways of interacting with their actively-changing environment. The difficulty in investigating these systems experimentally is a major scientific challenge that compel us to employ alternative approaches to analyze them. In my research program, we devise quantitative approaches (adopted from the field of Nonlinear Dynamics and Computational Biology) in parallel with experimental techniques, to study the rich dynamics of neurophysiological and immunological systems. That includes examining electrophysiological and hormonal rhythms in neurons within the brain, analyzing immune-cell response/kinetics and interactions at the single cell and population levels and understanding how subcellular processes (e.g. ion channels, receptors, proteins, etc.) govern the supracellular behavior of both systems within an integrated network. To accomplish this, we design computational tools (e.g. signal processing and machine learning algorithms) to analyze in vivo and in vitro data, and/or develop mathematical models along with quantitative methods to study their behavior. We construct in silico models based on our understanding of the physiological processes regulating them, followed by validating them against experimental data by designing inference-based fitting techniques. The latter is done by performing systematic model comparisons and parameter optimization to determine the most likely model and parameter set(s). Using steady state analysis, slow-fast analysis, numerical techniques and biophysical methods, the models are then investigated to decipher the behavior of physiological systems at the sub/supracellular and (meta)network levels, to provide insights about their general and emergent behavior, to identify causes of abnormalities within them, to test hypotheses and guide new experiments, to make experimentally testable predictions, and to develop technological tools to manipulate them. The models developed also raise intriguing mathematical questions that are tackled either numerically using computational tools that we design, or analytically using methods of nonlinear (stochastic) dynamics.

生理系统表现出不同程度的复杂性以及与不断变化的环境相互作用的迷人方式。通过实验研究这些系统的困难是一项重大的科学挑战，迫使我们采用替代方法来分析它们。在我的研究项目中，我们设计了与实验技术并行的定量方法（采用非线性动力学和计算生物学领域）来研究神经生理学和免疫系统的丰富动力学。这包括检查大脑内神经元的电生理和激素节律，分析单细胞和群体水平的免疫细胞反应/动力学和相互作用，以及了解亚细胞过程（例如离子通道、受体、蛋白质等）如何控制集成网络中两个系统的超细胞行为。为了实现这一目标，我们设计计算工具（例如信号处理和机器学习算法）来分析体内和体外数据，和/或开发数学模型以及定量方法来研究它们的行为。我们根据对调节模型的生理过程的理解构建计算机模型，然后通过设计基于推理的拟合技术根据实验数据验证它们。后者是通过执行系统模型比较和参数优化来确定最可能的模型和参数集来完成的。然后使用稳态分析、慢-快分析、数值技术和生物物理方法，对模型进行研究，以破译生理系统在亚/细胞上和（元）网络层面的行为，提供有关其一般和紧急行为的见解，识别其中异常的原因，测试假设并指导新实验，做出可通过实验测试的预测，并开发技术工具来操纵 他们。开发的模型还提出了有趣的数学问题，这些问题可以使用我们设计的计算工具进行数值解决，或者使用非线性（随机）动力学方法进行分析。