Yin is part of a grant to create and study a “digital twin” of the urinary tract

John Yin, the Vilas Distinguished Achievement Professor in chemical and biological engineering at the University of Wisconsin-Madison, is part of a $3 million, five-year grant from the National Institutes of Health to build a computerized model that could inform new, highly-targeted and less painful treatments for people with lower urinary tract conditions.

The research team includes principal investigator Zachary Danziger and other researchers at Florida International University as well as collaborators from the University of Missouri, Northeastern University and the United States Military Academy.

The researchers are building the model to investigate how the nervous system and urinary tract are connected. They hope the model will lead to an understanding of how electric stimulation of certain specific nerves could help people struggling with underactive bladders—one of the most common lower urinary tract problems—caused by different factors, such as aging, neurological disorders, Parkinson’s disease and more.

Controlling those nerves with electrical pulses could theoretically restore proper bladder function. The challenge is that researchers must pinpoint the correct nerve to make it work. The nervous system is highly complex, and an electrical signal to the wrong nerve could have unintended consequences, like causing a random twitch, raising blood pressure or creating pain.

The computerized model of the bladder and its surrounding areas will serve as a “digital twin” of the urinary tract, allowing researchers to perform experiments that are not possible to perform on humans.

The research will come down to a heavy dose of interdisciplinary work. In the urinary tract system, some things can be explicitly defined by math, such as how much liquid goes into the bladder and how much goes out. However, some aspects are still unknown, especially concerning the urinary tract’s neural controls. Here, artificial intelligence and machine learning will be used to connect the dots.

The research team expects to have new therapeutic predictions based on their theory and computational models within three to four years. In future work, the researchers will need to validate those predictions by conducting tests.

A previous version of this article was published by Florida International University.