Mathematical modeling of the lower urinary tract: A review.
Mathematical modeling of the lower urinary tract: A review.
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DOI：
10.1002/nau.24995
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发表时间：
2022-08
影响因子：
2
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中科院分区：
文献类型：
--
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了解在功能预测性下尿路(LUT)模型方面取得了哪些进展，确定了知识差距，并从这些差距中制定了前进的道路。我们综述了LUT基本组件(膀胱、尿路及其神经控制)的主要数学模型，并对与每个组件相关的常见建模策略和理论假设进行了分类。鉴于LUT功能是从这些组件的相互作用中产生的，我们强调了对它们之间的联系进行建模的尝试，并强调了LUT功能的未建模方面。目前还没有令人满意的完整的LUT模型来预测其在疾病、治疗或其他干扰下的功能。特别是，对LUT的神经控制缺乏基于生理学的数学描述。根据我们对迄今工作的调查，实现预测性LUT模型的一个潜在途径是模块化工作，其中最初使用可扩展和可互操作的方法建立单个组织水平组件的模型，允许它们在共同的框架中连接和测试。模块化方法将允许实现全面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研究的基础上，尊重组件之间的潜在交互作用，并激励对缺失组件进行建模的努力。使用模块化框架并基于生理学原理开发模型，创建功能预测模型是该领域准备迎接的挑战。
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.
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影响因子：
2
作者：
Hokanson JA;DeLancey JOL
通讯作者：
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影响因子：
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Erdemir A;Mulugeta L;Ku JP;Drach A;Horner M;Morrison TM;Peng GCY;Vadigepalli R;Lytton WW;Myers JG Jr
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Myers JG Jr
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Abrams, Paul
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通讯作者：
Manchanda, Rohit
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作者：
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通讯作者：
vanLeeuwen, JL