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

目标：融合深度学习和机械建模来分析昼夜节律时钟神经元、衰老、心律失常和阿尔茨海默病的电生理学

• 批准号: 2152115
• 负责人: Casey Diekman
• 金额: $ 46.41万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152115&HistoricalAwards=false
• 关键词: GOALI; Merging; Deep; Learning; Mechanistic

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.

这个与IBM Research的合作项目旨在从观测数据中构建模型的方法学方面取得突破性改进，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，医学努力通过药物等新疗法来影响它们来改善健康。机器学习现在可以根据来自不同来源的观测数据对生物现象做出惊人准确的预测。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来源的潜在生理机制。另一方面，机械模型复制实验和临床数据的特征，并使用代表潜在生理学的模型参数来解决其原因。但这些模型通常无法解决固有的细胞与细胞和患者与患者之间的变异性。该项目将开发一个混合深度学习/机械建模框架，可以通过识别导致模型输出与数据一致的参数集来捕获和解释生物数据中的固有可变性。该框架旨在具有足够的通用性，以找到由某个因素区分的多个条件的模型的输入参数（例如，治疗、年龄或疾病状态）;这样的“干预”场景在实践中是常见的。该框架将通过使研究人员能够根据有关干预性质的先前知识纳入额外的限制来推进最新技术水平。该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新型混合建模架构，该架构使用生成对抗网络（一种两个人工神经网络竞争的机器学习算法），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解决一系列重要的生物学问题，包括生物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，该系统将被用来确定哪些离子通道电导参与了视交叉上核神经元的昼夜节律幅度的年龄相关性下降和阿尔茨海默病小鼠模型中海马神经元的兴奋性改变。其次，该系统将用于人体心电图数据，以测试心脏兴奋性的昼夜节律可能影响用于治疗心律失常的药物的疗效的假设。学生从埃塞克斯县学院，一个开放式的，为期两年的学院，是联邦指定为少数民族服务机构，将进行暑期研究指导的研究人员团队从学术界（新泽西理工学院和普渡大学）和行业（IBM）在人工智能和深度学习，生物物理建模和模拟，和动力系统理论的互补专业知识。ECC学生以及NJIT研究生将通过与IBM的TJ沃森研究中心的互动来接触工业研究环境。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。