A New Paradigm for Systems Physiology Modeling: Biomechanistic Learning Augmentation with Deep Differential Equation Representations (BLADDER)

已经提出了许多有前途的外周神经调节技术来治疗低血压。 泌尿道（LUT）功能障碍，但我们缺乏预测模型，迫使社会 （包括PI的实验室）探索神经靶点的巨大参数空间，刺激 参数化，和电极设计经验，在动物实验中通过试验和错误。这 一种探索性实验是目前唯一的优化、个性化或 发现新的LUT神经调节技术。基于这种临床需求，我们的长期 这项工作的目标是预测神经调节对LUT的影响。 为了实现这一目标，我们建议开发一个新的建模框架， 通过机器学习建立不同的生物物理模型，从而模拟整个器官系统， 通过一个我们称之为深度微分方程的生物力学学习增强的过程， 代表（膀胱）。我们将开发和使用一般膀胱框架， 在其充盈和排泄周期中创建正常健康LUT的器官水平模型， 包括对膀胱和尿道的非自主神经反射控制。我们专注于神经 反射控制和器官水平尺度确保，如果成功，膀胱LUT模型将 准备使用计算研究来预测神经调节的效果，到目前为止， 由于LUT的复杂性，这是不可能的。 膀胱框架联合了多个单独的机制模型（每个模型考虑 器官系统的组成功能），通过使用深度递归神经网络（RNN）， 学习连接每个组件模型的适当耦合动力学。的组合 单一框架下的机械和机器学习模型使我们能够利用 两者的优点：机械模型擅长解释性，但缺乏可扩展性 （在器官系统层面变得棘手），而机器学习模型则非常出色 但缺乏普遍性和对假设生成的见解。膀胱 一个框架将通过将易处理的 使用监督RNN对组件进行建模，允许BLADDER框架 提供可解释性和规模。 我们将利用猫中现有的数据集（例如，Bruns和Gaunt），公开存在 在RAT中生成新的数据，以构建用于训练数据集。 监督RNN我们将进一步借鉴已经发表的小规模机械模型， 在人类和动物数据上对膀胱LUT的机械组件进行了验证 模型识别这些模型和数据集并检查其有效性的正式过程 和鲁棒性，将清楚地揭示我们的理论和实验的缺陷和优势， 以一种简单而理性的方式理解LUT。我们将使用10个简单的规则 审查纳入膀胱LUT模型的机制模型，并编制一份公共 神经泌尿学社区的清单。 主要任务1（Q1-2）：确定可用的数据集和候选机制模型， 出版的文献。主要的交付成果是一个公共数据库和一份详细介绍国家的白皮书 的领域和前景的建模和实验工作。 主要任务2（Q1-3）：证明膀胱框架的概念证明。主要 可交付成果是一个公开可用的代码，通过监督链接两个LUT组件模型 RNN和基于完全描述的动态系统的RNN架构报告。 主要任务3（Q3-6）：创建多组分膀胱模型。主要交付成果包括 用于通过监督RNN链接单独的机械LUT模型的代码，以及体内大鼠 数据集来填充机器学习训练集的关键可测量值。 主要任务4（Q6-8）：部署LUT的完全可操作膀胱模型，包括 自主预测神经反射控制。主要交付成果是公开可用的代码， 数据集和假设驱动的计算实验来预测简单的干预措施。

Many promising peripheral neuromodulation techniques have been proposed to treat lower urinary tract (LUT) dysfunction, but our lack of predictive models has forced the community (including the PI’s lab) to explore the vast parameter space of nerve targets, stimulation parameterizations, and electrode designs empirically in animal experiments by trial and error. This type of exploratory experimentation is the only current method of optimizing, personalizing, or discovering novel LUT neuromodulation techniques. Motivated by this clinical need, our long-term goal for this work is to predict the effects of neuromodulation on the LUT. To move toward this goal, we propose to develop a new modeling framework that integrates disparate biophysics models through machine learning, thereby emulating an entire organ system through a process we call Biomechanistic Learning Augmentation of Deep Differential Equation Representations (BLADDER). We will develop and use the general BLADDER framework to create an organ-level model of the normal healthy LUT throughout its filling and voiding cycles, including non-volitional neural reflex control over the bladder and urethra. Our focus on neural reflex control and organ-level scales ensures that, if successful, the BLADDER LUT model will be poised to predict effects of neuromodulation using computational studies, which so far has been impossible due to the complexity of the LUT. The BLADDER framework unites multiple individual mechanistic models (each accounting for a component function of an organ system) by using deep recurrent neural networks (RNN) to learn the appropriate coupling dynamics linking each component model. The combination of mechanistic and machine learning models under a single framework allows us to harness the advantages of both: mechanistic models excel at interpretability but suffer from a lack of scalability (becoming intractable at the level of organ systems), while machine learning models are excellent at scale but lack generalizability and insights for hypothesis generation. The BLADDER framework will scale up mechanistic models to the level of systems physiology by linking tractable model components together using a supervisory RNN, allowing the BLADDER framework to deliver both interpretability and scale. We will draw on existing SPARC datasets in the cat (e.g., Bruns and Gaunt), existing publicly available data in rat, and generate new data in the rat to construct a training dataset for the supervisory RNN. We will further draw from already published small-scale mechanistic models, validated on human and animal data, for the mechanistic components of the BLADDER LUT model. The formal process of identifying these models and datasets, and checking their validity and robustness, will clearly reveal the deficits and strengths in our theoretical and experimental understanding of the LUT in a straightforward and rational way. We will use the 10 Simple Rules to vet mechanistic models for inclusion in the BLADDER LUT model and compile a public inventory for the neurourology community. Major task 1 (Q1-2): Identify available datasets and candidate mechanistic models from published literature. Major deliverables are a public database and a whitepaper detailing the state of the field and prospects for modeling and experimental work. Major Task 2 (Q1-3): Demonstrate proof of concept of BLADDER framework. Major deliverables are a publicly available code linking two LUT component models via supervisory RNN and a report on suitable RNN architectures based on fully described dynamical systems. Major Task 3 (Q3-6): Create a multi-component BLADDER model. Major deliverables are code used to link separate mechanistic LUT models via the supervisory RNN, and an in vivo rat dataset to fill in critical measurables for the machine learning training set. Major Task 4 (Q6-8): Deploy the fully operational BLADDER model of the LUT, including autonomously predicted neural reflex control. Major deliverables are publicly available codes and datasets, and a hypothesis-driven computational experiment to predict simple interventions.