# Novel Hybrid Computational Models to Disentangle Complex Immune Responses

> **NIH NIH R01** · UNIVERSITY OF IDAHO · 2024 · $228,422

## Abstract

Most quantitative models in biomedical research have been formulated by ordinary differential equations 
(ODEs). Despite the great contributions ODEs have made to biology and beyond, the high-dimensional, 
time-dependent factors of the immune system still pose a significant challenge to the predictive value of 
ODEs as it would require several hundred equations and thousands of parameters to be estimated. The 
recent rise of machine learning as a powerful computational tool to integrate large datasets presents a 
special opportunity to deal with the inherent complexity of biological systems. However, machine learning 
approaches do not consider the mechanistic knowledge of the underlying interactions. Preliminary studies 
that combine ODEs and machine learning highlight that these computational algorithms could be on the 
cusp of a major revolution. Remarkably enough, however, no parameter estimation theory exists to 
integrate simultaneously both approaches. We propose to create new hybrid models and test their 
predictions in a mouse viral coinfection system to address a central vexation for infection biology which is 
how and when to modulate immune responses to mitigate mortality during lethal respiratory viral infection. 
At the interface between mathematical and life sciences, we will develop and analyze a novel suite of 
computational models that will integrate the underlying biological mechanisms to manage ill-posed 
problems and explore massive design spaces, allowing for robust predictions from complex biological 
systems. To validate and test our novel and foundational mathematical approaches, we will generate the 
biological data from a mouse infection system with a mild viral pathogen (rhinovirus) two days before 
infection with a lethal viral pathogen (influenza) that results in reduced disease compared to single infection 
alone. We hypothesize that this system can train our mathematical models in a natural way how the innate 
immune system can be manipulated to reduce mortality to lethal infections and beyond. Key model 
predictions will be tested by targeted immune system manipulation during lethal infection, paving the way to 
understanding the role of complex immune interactions in respiratory viral disease pathology.

## Key facts

- **NIH application ID:** 10935967
- **Project number:** 5R01GM152736-02
- **Recipient organization:** UNIVERSITY OF IDAHO
- **Principal Investigator:** Esteban Abelardo Hernandez Vargas
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $228,422
- **Award type:** 5
- **Project period:** 2023-09-26 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10935967

## Citation

> US National Institutes of Health, RePORTER application 10935967, Novel Hybrid Computational Models to Disentangle Complex Immune Responses (5R01GM152736-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10935967. Licensed CC0.

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