Deciphering the dynamics of physiological systems using quantitative and computational approaches

生理系统表现出不同程度的复杂性和迷人的方式与他们积极变化的环境相互作用。实验研究这些系统的困难是一个重大的科学挑战，迫使我们采用替代方法来分析它们。在我的研究计划中，我们设计了定量方法（从非线性动力学和计算生物学领域采用）与实验技术并行，以研究神经生理学和免疫系统的丰富动态。这包括检查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，并了解亚细胞过程（例如离子通道，受体，蛋白质等）在一个整合的网络中控制着两个系统的超细胞行为。为了实现这一目标，我们设计了计算工具（例如信号处理和机器学习算法）来分析体内和体外数据，和/或开发数学模型沿着定量方法来研究它们的行为。我们基于对调节它们的生理过程的理解来构建计算机模型，然后通过设计基于推理的拟合技术来验证它们对实验数据的影响。后者是通过执行系统的模型比较和参数优化来确定最可能的模型和参数集。使用稳态分析，慢-快分析，数值技术和生物物理方法，然后研究模型，以破译亚/超细胞和（Meta）网络水平上的生理系统的行为，提供有关其一般和紧急行为的见解，以确定其中异常的原因，测试假设并指导新的实验，进行实验可检验的预测，并开发技术工具来操纵它们。开发的模型也提出了有趣的数学问题，解决无论是数值计算工具，我们设计，或分析使用非线性（随机）动力学的方法。

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.