# Modeling Core

> **NIH NIH U19** · SCRIPPS RESEARCH INSTITUTE, THE · 2023 · $407,192

## Abstract

Project Summary - Modeling Core
The Center for Viral Systems Biology (CViSB) collects and aggregates complex, multidimensional, and
heterogeneous data, leading to challenges in interpreting these diverse experimental measurements. The
Modeling Core’s primary goal is to develop and apply computational and statistical methods to elucidate
key features of viral pathogens as well as host immune responses to infection and vaccination. Our
approach will integrate data both within and across measurement modalities using data-driven
mathematical tools to identify important host and virus factors, understand virus evolution and spread,
and predict patient outcomes. We will disseminate our findings and tools, enabling CViSB as well as
community researchers to perform similar analyses for a range of viral pathogens.
To accomplish these objectives and support the overall mission of CViSB, we have the following four
aims: (1) Build models to predict disease outcomes using data-driven latent variable models; (2) Develop
methods to study host-pathogen dynamics by integrating heterogeneous data; (3) Construct methods for
large-scale analysis of evolutionary dynamics; (4) Integrate social, environmental, and virus factors into
models to understand virus variant spread; and (5) Provide broad analysis support for CViSB and the
SysBio community.

## Key facts

- **NIH application ID:** 10558422
- **Project number:** 2U19AI135995-06
- **Recipient organization:** SCRIPPS RESEARCH INSTITUTE, THE
- **Principal Investigator:** DOUGLAS A LAUFFENBURGER
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $407,192
- **Award type:** 2
- **Project period:** 2018-02-01 → 2028-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10558422

## Citation

> US National Institutes of Health, RePORTER application 10558422, Modeling Core (2U19AI135995-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10558422. Licensed CC0.

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