PDF(Pubmed)
Abstract：
Understand what progress has been made toward a functionally predictive lower urinary tract (LUT) model, identify knowledge gaps, and develop from them a path forward.
We surveyed prominent mathematical models of the basic LUT components (bladder, urethra, and their neural control) and categorized the common modeling strategies and theoretical assumptions associated with each component. Given that LUT function emerges from the interaction of these components, we emphasized attempts to model their connections, and highlighted unmodeled aspects of LUT function.
There is currently no satisfactory model of the LUT in its entirety that can predict its function in response to disease, treatment, or other perturbations. In particular, there is a lack of physiologically based mathematical descriptions of the neural control of the LUT.
Based on our survey of the work to date, a potential path to a predictive LUT model is a modular effort in which models are initially built of individual tissue-level components using methods that are extensible and interoperable, allowing them to be connected and tested in a common framework. A modular approach will allow the larger goal of a comprehensive LUT model to be in sight while keeping individual efforts manageable, ensure new models can straightforwardly build on prior research, respect potential interactions between components, and incentivize efforts to model absent components. Using a modular framework and developing models based on physiological principles, to create a functionally predictive model is a challenge that the field is ready to undertake.
We surveyed prominent mathematical models of the basic LUT components (bladder, urethra, and their neural control) and categorized the common modeling strategies and theoretical assumptions associated with each component. Given that LUT function emerges from the interaction of these components, we emphasized attempts to model their connections, and highlighted unmodeled aspects of LUT function.
There is currently no satisfactory model of the LUT in its entirety that can predict its function in response to disease, treatment, or other perturbations. In particular, there is a lack of physiologically based mathematical descriptions of the neural control of the LUT.
Based on our survey of the work to date, a potential path to a predictive LUT model is a modular effort in which models are initially built of individual tissue-level components using methods that are extensible and interoperable, allowing them to be connected and tested in a common framework. A modular approach will allow the larger goal of a comprehensive LUT model to be in sight while keeping individual efforts manageable, ensure new models can straightforwardly build on prior research, respect potential interactions between components, and incentivize efforts to model absent components. Using a modular framework and developing models based on physiological principles, to create a functionally predictive model is a challenge that the field is ready to undertake.
摘要：
了解在功能预测下尿路（LUT）模型方面取得了哪些进展，确定知识差距，并从他们那里发展出一条前进的道路。
我们调查了基本LUT组件(膀胱，尿道,及其神经控制），并对与每个组件相关的常见建模策略和理论假设进行分类。鉴于LUT功能是由这些组件的相互作用产生的，我们强调了试图模拟他们的联系，并突出显示了LUT功能的未建模方面。
目前尚无令人满意的LUT整体模型可以预测其对疾病的反应功能，治疗,或其他扰动。特别是,LUT的神经控制缺乏基于生理学的数学描述。
根据我们对迄今为止工作的调查，预测LUT模型的潜在路径是一种模块化的努力，其中模型最初使用可扩展和可互操作的方法由单个组织级组件构建，允许它们在通用框架中连接和测试。模块化方法将允许实现全面的LUT模型的更大目标，同时保持个人工作的可管理性，确保新模型可以直接建立在先前的研究基础上，尊重组件之间的潜在相互作用，并激励为缺失组件建模的努力。使用模块化框架并基于生理原理开发模型，创建功能预测模型是该领域准备承担的挑战。
我们调查了基本LUT组件(膀胱，尿道,及其神经控制），并对与每个组件相关的常见建模策略和理论假设进行分类。鉴于LUT功能是由这些组件的相互作用产生的，我们强调了试图模拟他们的联系，并突出显示了LUT功能的未建模方面。
目前尚无令人满意的LUT整体模型可以预测其对疾病的反应功能，治疗,或其他扰动。特别是,LUT的神经控制缺乏基于生理学的数学描述。
根据我们对迄今为止工作的调查，预测LUT模型的潜在路径是一种模块化的努力，其中模型最初使用可扩展和可互操作的方法由单个组织级组件构建，允许它们在通用框架中连接和测试。模块化方法将允许实现全面的LUT模型的更大目标，同时保持个人工作的可管理性，确保新模型可以直接建立在先前的研究基础上，尊重组件之间的潜在相互作用，并激励为缺失组件建模的努力。使用模块化框架并基于生理原理开发模型，创建功能预测模型是该领域准备承担的挑战。
