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Mathematical Modeling, Simulation and Analysis

This program supports the development of novel mathematical modeling, simulation and analysis tools that can be broadly applied across a wide spectrum of diagnostic, therapeutic, imaging, and interventional applications.

Mathematical Modeling

Emphasis is on engineering solutions for theory-driven, physics-based, physiologically realistic, virtual representations of biomedical systems, with a particular weight on multiscale modeling. NIBIB interests include, but are not limited to:

  • multiscale modeling methodologies to bridge spatial and temporal scales
  • predictive modeling frameworks to facilitate the formation of testable hypothesis
  • non-standard methodologies that address modeling challenges, such as, uncertainty quantification, modularity, sparse data, scaling of species models, etc.
  • methods to address model credibility, reproducibility, and reuse by the biomedical community

This program supports the Interagency Modeling and Analysis Group (IMAG) and the Multiscale Modeling Consortium.


Emphasis is on engineering, mathematical, statistical and computational approaches for emulating system dynamics and processes implicated in biomedical applications, with a particular weight on medical simulator design and development to reduce medical errors and increase patient safety.  NIBIB interests include, but are not limited to:

  • virtual coaches incorporating artificial intelligence for performance training in medical procedures and workflows to provide real-time feedback to the end user
  • simulation interfaces to facilitate dissemination and use of virtual environments for end users
  • realistic representations of anatomy, tissue, instrument, tactile feedback, and collision dynamics
  • simulator designs that focus on complicated or rare procedures, or rare adverse events
  • simulators that replicate “real life” work flows, including planning, warm-up exercises, and rehearsal leading up to the actual procedure
  • portable, easy-to-use simulators for skilled practitioners in rural and low-resource settings

This program has shared interest with the NIBIB Surgical Tools Program.


Emphasis is on theoretical, mathematical, statistical and engineering approaches to interpret the behavioral of complex biomedical data and its dynamics, with a particular weight on paradigm shifting methodologies and software interfaces.  NIBIB interests include, but are not limited to:

  • intelligent control systems approaches of medical devices and hardware systems
  • novel methods to extract fundamental dynamical (mechanistic) features and patterns from large nonlinear, spatio-temporal datasets for real-time data analysis
  • novel implementations of dynamic versions of principal component analysis
  • tools to address data dimensionality, data fusion, and data assimilation methods to combine heterogeneous data and link sparse data with mechanisms
  • formal statistical inference frameworks to conduct network connectivity and causal-inference analyses from different types of biomedical data

The NIH BRAIN Initiative supports many projects in this program.

Applications proposing to use, rather than develop Mathematical Modeling, Simulation and Analysis tools should be referred to another institute.

Grant Number Project Title Principal Investigator Institution
5-R21-EB023051-02 New fractional calculus models of attenuation for shear wave elasticity imaging Robert Mcgough Michigan State University
1-R41-EB026358-01A1 Development and Validation of a Physical Anatomic Model for Surgical TrainingUsing 3D Printing Technology Jonathan Stone Simulated Inanimate Models, Llc
1-R43-EB027525-01 PostureCheck: A vision-based compensatory-posture-detection tool to enhance performance of the BURT® upper-extremity stroke-therapy device Alexandros Lioulemes Barrett Technology, Llc
5-T32-EB009403-10 Integrated, Interdisciplinary, Inter-university PHD Program Computational Biology Russell Schwartz Carnegie-Mellon University
5-U01-EB024501-02 Modular design of multiscale models, with an application to the innate immune response to fungal respiratory pathogens. Reinhard Laubenbacher University of Connecticut Sch of Med/Dnt
5-U54-EB020405-05 Mobility Data Integration to Insight Scott Delp Stanford University
5-R01-EB022864-03 Toward a Theory for Macroscopic Neural Computation Based on Laplace Transform Marc Howard Boston University (Charles River Campus)
5-R01-EB022915-03 Bayesian estimation of network connectivity and motifs Dario Ringach University of California Los Angeles
5-R01-EB022726-03 Filtered Point Process Inference Framework for Modeling Neural Data Emery Brown Massachusetts General Hospital
5-P41-EB001978-33 Biomedical Simulations Resource (BMSR) David D'Argenio University of Southern California