Juan Cui
University of Nebraska-Lincoln, NE·Biomedical Engineering
Skeletal muscle does more than move the body; it is central to strength and to healthy metabolism. When muscle stops responding well to exercise, to aging, to illness, or during recovery from injury, people lose strength and face serious health consequences.
- Fields
Biomedical Engineering
Computer Science
Physiology
- Topics
skeletal muscle
digital twin
muscle adaptation
- Grants
- 1 known grant, through Sep 2029
What they work on
- Build a hybrid mechanistic and artificial-intelligence digital twin of skeletal muscle that predicts adaptation across different activity levels and metabolic states, organized around five interacting modules spanning mechanical loading, protein turnover, inflammation, regeneration and metabolism.
- Generate controlled perturbation data from a mouse physical twin with multiscale molecular, structural and functional muscle measurements to train and constrain the digital twin.
- Collect longitudinal physiological and behavioral data from a human physical twin to translate and personalize model predictions from mouse to person.
- Develop methods for estimating unobserved physiological states from sparse, irregular, multimodal data, combining continuous-time state-space models, Bayesian data assimilation, latent-state inference and learned residual dynamics.
- Create adaptive verification, validation and uncertainty quantification approaches and assess how well the model transfers across species and across types of perturbation.
More about the research
Predicting how an individual's muscle will adapt is hard because the relevant processes span molecules to whole-body behavior and are measured only sparsely. This project proposes to build a digital twin of skeletal muscle, a computational replica that is continuously updated with real measurements, to predict how muscle adapts across different levels of activity and metabolic stress.
Why it matters
Muscle weakness accompanying aging, illness and injury recovery leads to loss of independence and other health problems. A validated digital twin that predicts how an individual's muscle responds to activity and metabolic stress could support rehabilitation planning, strategies for healthy aging, and precision health approaches. By making simulations more informative, the work may also reduce the number of animal experiments needed, and it contributes reusable modeling methods for other partially observed physiological systems.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Christophe Lenglet
University of Minnesota, MN·Biomedical Engineering
Develop low-dimensional manifold learning, causal pathway search and feature selection methods for digital twin models built from brain MRI of neurodegenerative disease.
Adrian Sandu
Virginia Polytechnic Institute and State University, VA·Computer Science
Develop high-fidelity multiphysics models of jet engine combustion and flow together with scalable reduced-order models, both with quantified uncertainties, as the computational core of a predictive digital twin.
Clare Fitzpatrick
Boise State University, ID·Biomedical Engineering
Build a multiscale computational model of the leg that links cortical and spinal control of muscles with muscle physiology, bone and joint mechanics, and the effects of pain on movement.
The school
University of Nebraska-Lincoln: 17 labs on PI Pages, $657M a year on research, 261 research doctorates a year.
Reach out
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