# A predictive multi-scale model of the immune system: An integrated computational resource for interdisciplinary applications.

> **NIH NIH R35** · UNIVERSITY OF NEBRASKA LINCOLN · 2020 · $355,364

## Abstract

Abstract
Diseases are often a result of multiple malfunctions in complex, nonlinear network systems that span
multiple layers of biological organization, ranging from molecular to cellular to organ and organismal
levels. The immune system is no exception. Its proper response to foreign stimuli is governed by
network-like interactions among various types of cells and cytokines as their communication
mediators. The complexity at the inter-cellular level of the immune system is further exacerbated by
the similarly complex biological and biochemical networks within each cell (metabolism, gene
regulation, etc.) that are responsible for the dynamics and decision-making at the single-cell level.
Despite substantive research efforts in systems immunology, existing computational models are
limited to network models at individual molecular or cellular scales and/or focus on a single disease
within a small part of the immune system. Herein, we propose to develop a systems-level,
comprehensive, and integrative computational framework for the immune system that is needed to
better understand and predict complex behavior of the immune system in the context of diseases and
associated therapies. This framework will integrate data and knowledge across various levels of
biological organization, capture nonlinear dynamics, and incorporate and facilitate mechanistic
understanding. Such a framework has the potential to enable the interrogation of the dynamics and
emergent properties of complex molecular, cellular, and disease networks that give rise to and
regulate the immune system. This computational resource will provide a broad environment to a range
of scientific communities, including molecular experimentalists, clinicians, translational scientists, and
computational biologists. Furthermore, our group will utilize the comprehensive model to better
understand emergent properties that underlie the immune system, including immune memory,
adaptation, etc. Finally, we will also investigate the capacity, plasticity, and richness of T-cell
differentiation. We hypothesize that additional cytokine profiles defining new CD4+ effector T cells exist
and that the underlying phenotypes exhibit flexibility to provide more dynamics to immune response.
For example, we expect to identify specific combinations of extracellular signals that are able to
stimulate one type of CD4+ T cells to switch to another type, as well as identify novel patterns of
cytokine profiles that may correspond to additional T cell types.

## Key facts

- **NIH application ID:** 9963287
- **Project number:** 5R35GM119770-05
- **Recipient organization:** UNIVERSITY OF NEBRASKA LINCOLN
- **Principal Investigator:** Tomas Helikar
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $355,364
- **Award type:** 5
- **Project period:** 2016-09-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9963287

## Citation

> US National Institutes of Health, RePORTER application 9963287, A predictive multi-scale model of the immune system: An integrated computational resource for interdisciplinary applications. (5R35GM119770-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9963287. Licensed CC0.

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