Data-driven and science-informed methods for the discovery of biomedical mechanisms and processes

用于发现生物医学机制和过程的数据驱动和科学信息方法

• 批准号: 10624014
• 负责人: David Bortz
• 金额: $ 34.9万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10624014
• 关键词: Biochemical; Biological; COVID-19; Cations; Cells; Classification; Climate; Collaborations; Communicable Diseases; Complex; Coupling; Data; Decision Making; Epidemiologist; Equation; Generations; Goals; Individual; Infection; Lead; Learning; Methodology; Methods; Modeling; Motion; Noise; Nonlinear Dynamics; Organism; Process; Recovery; Research; Robotics; Science; Seminal; System; cell motility; cell type; data-driven model; dynamic system; experimental study; laptop; learning strategy; member; migration; novel; success; timeline; wound healing

Abstract Text Data-driven discovery methods are a novel class of methodologies and computational approaches, revolutionizing the modeling, prediction, and control of complex systems, while remaining scientifically explainable and interpretable. These methods learn governing equations directly from data and have found considerable success in a wide range of applications including turbulence, climate, robotics, and autonomy. However, the first generation of these methods has proven poorly suited to the study of biomedical data. To realize the full potential of data-driven approaches, they must be extended and adapted to deal with the noise, sparsity, and variability intrinsic to experiments with living organisms. My group has extended the seminal Sparse Identification of Nonlinear Dynamics (SINDy) method to the Weak form SINDy (WSINDy). Weak form equations are a transform of the original data that enables learning of the equations even in the presence of substantial noise and sparsity. The approach effectively recasts scientific discovery from proposing and validating/refuting a single scientific hypothesis to simultaneously proposing (in many cases) more than 10^180 hypotheses and using sparse regressing to prune the hypotheses which are not supported by the data. Moreover, our approach currently takes on the order of minutes on a standard laptop. The overarching goals of this research are to use the WSINDy method to investigate the 1) individual cell-based drivers for collective cell migration and 2) data-driven inference for unobserved processes in infectious disease dynamics as well as 3) extend WSINDy to infer stochastic dynamical systems and discover critical, but hidden, compartments. The first goal continues a long collaboration with Xuedong Liu (CU-Boulder). We have adapted WSINDy to create individualized models of each cell in a migrating colony. We learn the interaction rules and can classify them according to cell type. The plan is to continue expanding the capabilities of WSINDy in this context to hopefully learn the biochemical dynamics unique to each cell. This would be the first coupling of data-driven models for inter- and intra-cell processes. It will lead us closer to understanding how cells make decisions that lead to the emergent collective motion in wound healing. The second goal expands a collaboration with Beth Carlton (an epidemiologist) in infectious disease dynamics centered around the COVID-19 modeling team (of which we are both members). During our efforts to develop a compartmental model for advising the State Epidemiologist and the Governor, several questions arose that could be efficiently answered by extensions to WSINDy. In particular, we will develop data-driven inference for infection and recovery rates as well as the distribution of dwell times in the infection timeline. The last goal involves extensions of WSINDy to learn models for situations that frequently arise in biomedical phenomenon. First, we plan to learn stochastic dynamical systems. Previous efforts were only able to infer either drift term or mean field equations. By recasting WSINDy to evaluate moments of the data, we can learn the stochastic models directly. Lastly, inference regarding unobserved compartments is challenging, but via an extension to WSINDy we plan to discover unobserved variables and their equations.

抽象文本 数据驱动的发现方法是一种新颖的方法和计算方法，彻底改变了对复杂系统的建模，预测和控制，同时仍然可以科学地解释和可解释。这些方法直接从数据中学习控制方程式，并在包括湍流，气候，机器人技术和自主权在内的广泛应用中发现了巨大的成功。但是，这些方法的第一代已被证明不适合研究生物医学数据。为了实现数据驱动方法的全部潜力，必须扩展和适应它们，以应对生物实验的噪声，稀疏性和可变性。我的小组已将非线性动力学（Sindy）方法的稀疏稀疏识别扩展到弱形式的sindy（wsindy）。弱形式方程是原始数据的转换，即使在存在很大的噪音和稀疏性的情况下，也能够学习方程式。该方法从提出和验证/反驳单一的科学假设中有效地重塑了科学发现，以同时提出（在许多情况下）（在许多情况下）超过10^180个假设，并使用稀疏的回归来修剪数据不支持数据的假设。此外，我们的方法目前采用标准笔记本电脑上的几分钟顺序。这项研究的总体目标是使用WSINDY方法来研究1）基于单元的单个驱动因素，用于集体细胞迁移以及2）数据驱动的推断感染性疾病动力学中未观察到的过程以及3）扩展WSINDY以推断WSINDY以推断随机动态系统，并发现关键，但隐藏了隔间。第一个进球继续与Xuedong Liu（Cu-Boulder）进行了长时间的合作。我们已经适应了Wsindy，以在迁移菌落中创建每个细胞的个性化模型。我们学习交互规则，并可以根据单元类型对它们进行分类。该计划是在这种情况下继续扩展Wsindy的功能，希望学习每个细胞独有的生化动态。这将是用于数据驱动模型的第一个耦合和内部细胞内过程的耦合。这将使我们更加了解细胞如何做出决定导致伤口愈合中出现的集体运动。第二个目标扩大了与贝丝·卡尔顿（Beth Carlton）（一名流行病学家）的合作，以围绕Covid-19建模团队（我们都是成员）为中心的传染病动力学。在我们开发一个为州流行病学家和州长提供建议的隔间模型的努力中，出现了几个问题，可以通过向Wsindy扩展有效地回答这些问题。特别是，我们将开发有关感染和恢复率的数据驱动推断，以及在感染时间表中的居住时间的分布。最后一个目标涉及Wsindy的扩展，以学习在生物医学现象中经常出现的情况的模型。首先，我们计划学习随机动力学系统。以前的努力只能推断漂移术语或平均场方程。通过重新铸造Wsindy来评估数据矩，我们可以直接学习随机模型。最后，关于未观察到的隔室的推论是具有挑战性的，但是通过向Wsindy扩展，我们计划发现未观察到的变量及其方程。