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

> **NIH NIH OT2** · FLORIDA INTERNATIONAL UNIVERSITY · 2020 · $1,025,141

## Abstract

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 inclusi...

## Key facts

- **NIH application ID:** 10206953
- **Project number:** 1OT2OD030524-01
- **Recipient organization:** FLORIDA INTERNATIONAL UNIVERSITY
- **Principal Investigator:** Zachary C Danziger
- **Activity code:** OT2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,025,141
- **Award type:** 1
- **Project period:** 2020-09-16 → 2023-09-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10206953

## Citation

> US National Institutes of Health, RePORTER application 10206953, A New Paradigm for Systems Physiology Modeling: Biomechanistic Learning Augmentation with Deep Differential Equation Representations (BLADDER) (1OT2OD030524-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10206953. Licensed CC0.

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