ArticleTheoretical biology & medical modelling2011
Using an agent-based model to analyze the dynamic communication network of the immune response.
Article in Theoretical biology & medical modelling, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 81 citations in OpenAlex.
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- Mathematical Modeling of Proliferative Immune Response Initiated by Interactions Between Classical Antigen-Presenting Cells Under Joint Antagonistic IL-2 and IL-4 Signaling.Frontiers in molecular biosciences · 2022Article
- Treelet transform analysis to identify clusters of systemic inflammatory variance in a population with moderate-to-severe traumatic brain injury.Brain, behavior, and immunity · 2021Article
- Leveraging Prior Knowledge to Recover Characteristic Immune Regulatory Motifs in Gulf War Illness.Frontiers in physiology · 2020Article
- Review of the systems biology of the immune system using agent-based models.IET systems biology · 2018Review
- A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins: part 1-theoretical model.CPT: pharmacometrics & systems pharmacology · 2014Article
- A role for homeostatic drive in the perpetuation of complex chronic illness: Gulf War Illness and chronic fatigue syndrome.PloS one · 2014Article
- On the coupling of two models of the human immune response to an antigen.BioMed research international · 2014Article
- Moving H5N1 studies into the era of systems biology.Virus research · 2013Review
- Systems approaches to influenza-virus host interactions and the pathogenesis of highly virulent and pandemic viruses.Seminars in immunology · 2013Review
- Multiscale computational models of complex biological systems.Annual review of biomedical engineering · 2013Review
- Development of immune-specific interaction potentials and their application in the multi-agent-system VaccImm.PloS one · 2011Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundThe immune system behaves like a complex, dynamic network with interacting elements including leukocytes, cytokines, and chemokines. While the immune system is broadly distributed, leukocytes must communicate effectively to respond to a pathological challenge. The Basic Immune Simulator 2010 contains agents representing leukocytes and tissue cells, signals representing cytokines, chemokines, and pathogens, and virtual spaces representing organ tissue, lymphoid tissue, and blood. Agents interact dynamically in the compartments in response to infection of the virtual tissue. Agent behavior is imposed by logical rules derived from the scientific literature. The model captured the agent-to-agent contact history, and from this the network topology and the interactions resulting in successful versus failed viral clearance were identified. This model served to integrate existing knowledge and allowed us to examine the immune response from a novel perspective directed at exploiting complex dynamics, ultimately for the design of therapeutic interventions.
resultsAnalyzing the evolution of agent-agent interactions at incremental time points from identical initial conditions revealed novel features of immune communication associated with successful and failed outcomes. There were fewer contacts between agents for simulations ending in viral elimination (win) versus persistent infection (loss), due to the removal of infected agents. However, early cellular interactions preceded successful clearance of infection. Specifically, more Dendritic Agent interactions with TCell and BCell Agents, and more BCell Agent interactions with TCell Agents early in the simulation were associated with the immune win outcome. The Dendritic Agents greatly influenced the outcome, confirming them as hub agents of the immune network. In addition, unexpectedly high frequencies of Dendritic Agent-self interactions occurred in the lymphoid compartment late in the loss outcomes.
conclusionsAn agent-based model capturing several key aspects of complex system dynamics was used to study the emergent properties of the immune response to viral infection. Specific patterns of interactions between leukocyte agents occurring early in the response significantly improved outcome. More interactions at later stages correlated with persistent inflammation and infection. These simulation experiments highlight the importance of commonly overlooked aspects of the immune response and provide insight into these processes at a resolution level exceeding the capabilities of current laboratory technologies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.