The National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health (NIH), recently awarded a five-year, $11.9 million contract to the University of Rochester (UR) Medical Center to continue the work of the Center for Biodefense Immune Modeling (CBIM). The co-directors of the UR-CBIM are Hulin Wu, a statistician and biostatistics professor, and Martin Zand, a professor in the department of medicine at the UR Medical Center.

This renewed proposal builds on the work of the UR-CBIM, which was established five years ago with an award of $10 million. The UR-CBIM—composed of a multidisciplinary team of statisticians, mathematical and engineering modelers, computer scientists, software developers, bioinformaticians, and biomedical investigators—sought to develop novel mathematical models, statistical methods, and bioinformatics tools to decipher the host immune responses to influenza infection and other infectious agents. Extensive experimental data from mice were collected to support the development and validation of the proposed models, statistical methods, and bioinformatics tools. This project resulted in 60 manuscripts, many of which were published in journals such as the Journal of the American Statistical Association, Annals of Statistics, Biometrika, and Biometrics.

The developed bioinformatics tools include a differential equation modeling tool, DEDiscover, which is user-friendly software for modelers and biomedical scientists that provides model simulation, parameter estimation, and model evaluation functions. It is freely available at the UR-CBIM website.

Extensive experimental data for model development and validation also were generated and will be available via the NIAID/NIH immunology database, ImmPort, and the UR-CBIM website.

Over the next five years, the UR-CBIM aims to develop multilevel and multitype mathematical/computational models for immune responses that include ordinary differential equation models, stochastic differential equation models, state-space models, stochastic process models, agent-based models, and network models at the cellular, protein, and genetic levels. Novel statistical methods will be developed for these complex dynamic models that are used to quantify immune responses to influenza infection and vaccination. Extensive experiments in both mice and human subjects will be performed, and high-throughput experimental data at cellular, protein, and genetic levels will be collected to support development, validation, and testing of the proposed models and methods.

The developed modeling techniques and statistical methods will be converted into user-friendly tools for biomedical investigators and modelers so they can better understand immune responses and design new experiments for discovering immune-based therapies and vaccines against infectious agents.