Mathematical modeling of the lower urinary tract: A review. Issue 6 (26th June 2022)

• Record Type: Journal Article
• Authors: Jaskowak, Daniel; Nunez, Roberto; Ramachandran, Rahul; Alhajjar, Elie; Yin, John; Guidoboni, Giovanna; Danziger, Zachary C.
• Is Part Of: Neurourology and urodynamics. Volume 41:Issue 6(2022)
• Page Start: 1305
• Page End: 1315
• Publication Date: 2022-06-26
• Subjects: bladder biomechanics -- computational model -- modularity and interoperability -- multiscale modeling -- neurourology -- predictive medicine -- systems physiology; Urinary organs -- Periodicals; Urodynamics -- Periodicals; Urology -- Periodicals; 616.6
• DOI: 10.1002/nau.24995
• Languages: English
• ISSNs: 0733-2467

Abstract

Abstract: Aims: 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. Methods: 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. Results: 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. Conclusions: 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.