GOALI: Merging Deep Learning and Mechanistic Modeling to Analyze the Electrophysiology of Circadian Clock Neurons, Aging, Cardiac Arrhythmias, and Alzheimer's Disease

这个与IBM研究院的合作项目旨在从观测数据中建立模型的方法上取得突破性的进步，这些模型既可以预测也可以解释生物现象。生命和健康科学的一个基本挑战是解释隐藏的生理和疾病机制。这些隐藏的机制塑造了实验和临床观察结果，医学努力通过药物等新疗法影响它们来改善健康。机器学习现在可以根据从不同来源观察到的数据，对生物现象做出惊人的准确预测。然而，这些预测很难解释和利用，因为它们通常不涉及数据和预测所依据的潜在生理机制。另一方面，机制模型复制实验和临床数据的特征，并通过代表潜在生理学的模型参数解决其原因。但这些模型通常无法解决细胞间和患者间的内在差异。该项目将开发一个混合深度学习/机制建模框架，通过识别参数集来捕获和解释生物数据的内在可变性，从而使模型输出与数据一致。该框架旨在具有足够的通用性，以便为同时由某些因素（例如，治疗、年龄或疾病状态）区分的多种条件找到模型的输入参数；这种“干预”场景在实践中很常见。该框架将使研究人员能够根据对干预性质的先验知识纳入额外的约束，从而推动目前的研究水平。这个项目将为社区大学和研究生提供跨学科的工业研究经验。该项目将开发和应用使用生成对抗网络的新型混合建模架构，生成对抗网络是一类机器学习算法，其中两个人工神经网络竞争，将实验观察的分布映射到生物物理模型参数的分布。该系统将使用实验合作者提供的数据集来解决一系列重要的生物学问题，包括昼夜节律钟神经元的电生理学和衰老、心律失常和阿尔茨海默病。首先，该系统将用于确定哪些离子通道电导参与了与年龄相关的视交叉上核神经元昼夜节律幅度的下降以及阿尔茨海默病小鼠模型中海马神经元兴奋性的改变。其次，该系统将用于人体心电图数据，以验证心脏兴奋性的昼夜节律会影响治疗心律失常药物疗效的假设。埃塞克斯郡学院是一所开放获取的两年制大学，被联邦政府指定为少数族裔服务机构。学生们将在来自学术界（新泽西理工学院和普渡大学）和工业界（IBM）的研究人员团队的指导下进行暑期研究，他们在人工智能和深度学习、生物物理建模和仿真以及动力系统理论方面具有互补的专业知识。ECC的学生，以及新泽西理工大学的研究生，将通过与IBM的T.J.沃森研究中心的互动，接触到工业研究环境。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This collaborative project with IBM Research aims to make breakthrough improvements in methodologies for building models from observational data that can both predict and explain biological phenomena. A fundamental challenge in the life and health sciences is explaining hidden physiological and disease mechanisms. These hidden mechanisms shape experimental and clinical observations, and medicine strives to improve health by influencing them with new therapies such as drugs. Machine learning can now make stunningly accurate predictions of biological phenomena based on observed data from variable sources. These predictions, however, are difficult to interpret and exploit, because they usually do not address the underlying physiological mechanisms from which the data and predictions derive. On the other hand, mechanistic models replicate features of the experimental and clinical data and address their causes with model parameters that represent the underlying physiology. But these models usually fail to address inherent cell-to-cell and patient-to-patient variability. This project will develop a hybrid deep learning/mechanistic modeling framework that can capture and explain the inherent variability in biological data through identification of parameter sets that result in model outputs consistent with data. The framework is intended to be versatile enough to find input parameters of a model for multiple conditions distinguished by some factor (e.g., treatment, age, or disease state) simultaneously; such “intervention” scenarios are common in practice. The framework will advance the state of the art by enabling researchers to incorporate additional constraints based on prior knowledge about the nature of an intervention. This project will provide interdisciplinary industrial research experiences to community college and graduate students.This project will develop and apply novel hybrid modeling architectures that use generative adversarial networks, a class of machine learning algorithms in which two artificial neural networks compete, to map distributions of experimental observations to distributions of biophysical model parameters. The system will tackle a set of important biological questions involving the electrophysiology of circadian clock neurons and aging, cardiac arrhythmias, and Alzheimer’s disease using datasets provided by experimental collaborators. First, the system will be employed to identify which ion channel conductances are involved in the age-related decline of circadian rhythm amplitude in suprachiasmatic nucleus neurons and the altered excitability properties of hippocampal neurons in mouse models of Alzheimer’s disease. Second, the system will be employed on human electrocardiogram data to test the hypothesis that circadian rhythms in cardiac excitability can affect the efficacy of drugs used to treat cardiac arrhythmias. Students from Essex County College, an open-access, two-year college that is federally designated as a minority serving institution, will perform summer research mentored by a team of researchers from academia (New Jersey Institute of Technology and Purdue University) and industry (IBM) with complementary expertise in artificial intelligence and deep learning, biophysical modeling and simulation, and dynamical systems theory. ECC students, as well as NJIT graduate students, will gain exposure to the industrial research environment through interactions with IBM’s T.J. Watson Research Center.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.