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.

Public Health Relevance

The proper response of the immune system to pathogens is governed by complex, network-like interactions among various types of cells and their associated communication mediators. Computational systems modeling provides the ability to describe and interrogate the dynamics of these complex systems with the objective to identify and design more sophisticated and effective drug therapies. We propose to build and provide a comprehensive computational resource for simulations, vizualization, and interrogation of the dynamics of a virtual immune system -- a comprehensive computational model that can be used to study not only the properties of the immune system, but also any associated diseases.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Unknown (R35)
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Special Emphasis Panel (ZRG1)
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Brazhnik, Paul
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University of Nebraska Lincoln
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United States
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