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  3. Innate Immune Response Agent-based Model (IIRABM)

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Innate Immune Response Agent-based Model (IIRABM)

What is being modeled?
Systemic Inflammation represented at the endothelial-blood interface
Description & purpose of resource

The Innate Immune Response Agent-Based Model (IIRABM) is a two-dimensional abstract representation of the human endothelial-blood interface.  This abstraction is designed to model the endothelial-blood interface for a traumatic (in the medical sense) injury, and does so by representing this interface as the unwrapped internal vascular surface of a 2D projection of the terminus for a branch of the arterial vascular network. The IIRABM operates by simulating multiple cell types and their interactions, including endothelial cells, macrophages, neutrophils, TH0, TH1, and TH2 cells as well as their associated precursor cells.  The simulated system dies when total damage (defined as aggregate endothelial cell damage) exceeds a pre-defined threshold.

Spatial scales
molecular
cellular
tissue
whole organism
Temporal scales
1 - 103 s
hours
days
weeks to months
This resource is currently
mature and useful in ongoing research
a demonstration or a framework to be built upon (perhaps with a sample implementation)
Has this resource been validated?
Yes
How has the resource been validated?

Validated against published clinical and experimental data

Link to Resource Credibility Assessment
Agent-based Models to address the Crisis of Reproducibility and Precision Medicine
Can this resource be associated with other resources? (e.g.: modular models, linked tools and platforms)
No
Key publications (e.g. describing or using resource)

  1. An G. In-silico experiments of existing and hypothetical cytokine-directed clinical trials using agent based modeling. Critical Care Medicine 2004; 32(10): 2050-2060. PMID: 15483414

  2. Cockrell C, An G. Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model. J Theor Biol. 2017 Jul 18;430:157-168. doi: 10.1016/j.jtbi.2017.07.016. [Epub ahead of print]: PMID:28728997

  3. Cockrell C, An, G. Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation. PLoS Comput Biol 2018 2018 Feb 15; 14(2): e1005876. https://doi.org/10.1371/journal.pcbi.1005876

  4. Petersen BK, Yang, J, Grathwohl WS, Cockrell C, Santiago C, An G and Faissol DM. Deep Reinforcement Learning and Simulation as Path Towards Precision Medicine. Journal of Computational Biology, 25 Jan 2019 Published Online. Doi: 10.1089?cmb.2018.0168

  5. Cockrell C, Ozik J, Collier N, An G. Nested Active Learning for Efficient Model Contextualization and Parameterization: Pathway to generating simulated populations using multi-scale computational models. Simulation. 2019 May 21:0037549720975075.

  6. Cockrell C and An G: Using Genetic Algorithms to reproduce the heterogeneity of clinical data through model refinement and rule discovery in a high-dimensional agent-based model of systemic inflammation. Frontiers in Physiology: Computational Physiology and Medicine. Accepted for Publication April 27, 2021

DOI link to publication describing this resource

DOI: 10.1016/j.jtbi.2017.07.016

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Model type
agent-based
Data type
systemic