Agent-Based Modeling and High Performance Computing

Maksudul Alam; Vida Abedi; Josep Bassaganya-Riera; Katherine Wendelsdorf; Keith Bisset; Xinwei Deng; Stephen Eubank; Raquel Hontecillas; Stefan Hoops; Madhav Marathe

doi:10.1016/B978-0-12-803697-6.00006-0

Agent-Based Modeling and High Performance Computing

Maksudul Alam, Vida Abedi, Josep Bassaganya-Riera, Katherine Wendelsdorf, Keith Bisset, Xinwei Deng, Stephen Eubank, Raquel Hontecillas, Stefan Hoops, Madhav Marathe

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

8 Scopus citations

Abstract

Agent-based modeling is a computational modeling framework for simulating the interactions of multiple diverse agents representing a complex system or phenomenon. The appoach has been successfully used to model a number of social, biological, and technological systems. In this chapter, we present ENteric Immunity SImulator (ENISI), an agent-based modeling framework for studying the inflammatory and regulatory immune pathways triggered by interactions among microbes and immune cells in the gut. In ENISI, individual cells move through simulated tissues and engage in context-dependent interactions with the other cells with which they are in contact. The scale of ENISI is unprecedented in this domain, with the ability to simulate 10⁷-10⁹ cells for 250 simulated days in 90min on a modest cluster. We describe the formal representation of the immune system as an agent-based model for modeling mucosal immune responses to gastrointestinal pathogens. We also describe performance and sensitivity analysis techniques and demonstrate the utility of ENISI in guiding the design of wet-lab experiments.

Original language	English
Title of host publication	Computational Immunology
Subtitle of host publication	Models and Tools
Publisher	Elsevier Inc.
Pages	79-111
Number of pages	33
ISBN (Electronic)	9780128037157
ISBN (Print)	9780128036976
DOIs	https://doi.org/10.1016/B978-0-12-803697-6.00006-0
State	Published - 2016
Externally published	Yes

Funding

We thank our external collaborators and members of the Network Dynamics and Simulation Science Laboratory for their suggestions and comments. This work has been partially supported by DTRA Grant HDTRA1-11-1-0016, DTRA CNIMS Contract HDTRA1-11-D-0016-0001, NIH MIDAS Grant 2U01GM070694-09, NSF NetSe CNS-1011769. This work was also supported in part by National Institute of Allergy and Infectious Diseases Contract No. HHSN272201000056C to J. Bassaganya-Riera and funds from the Nutritional Immunology and Molecular Medicine Laboratory ( www.nimml.org ).

Funders	Funder number
Nutritional Immunology and Molecular Medicine Laboratory
National Science Foundation	CNS-1011769
National Institutes of Health	2U01GM070694-09
National Institute of Allergy and Infectious Diseases	HHSN272201000056C

Keywords

Agent-based models
Data analytics
Dynamic systems
Graph
High performance computing
Sensitivity analysis

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10.1016/B978-0-12-803697-6.00006-0

Cite this

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title = "Agent-Based Modeling and High Performance Computing",

abstract = "Agent-based modeling is a computational modeling framework for simulating the interactions of multiple diverse agents representing a complex system or phenomenon. The appoach has been successfully used to model a number of social, biological, and technological systems. In this chapter, we present ENteric Immunity SImulator (ENISI), an agent-based modeling framework for studying the inflammatory and regulatory immune pathways triggered by interactions among microbes and immune cells in the gut. In ENISI, individual cells move through simulated tissues and engage in context-dependent interactions with the other cells with which they are in contact. The scale of ENISI is unprecedented in this domain, with the ability to simulate 107-109 cells for 250 simulated days in 90min on a modest cluster. We describe the formal representation of the immune system as an agent-based model for modeling mucosal immune responses to gastrointestinal pathogens. We also describe performance and sensitivity analysis techniques and demonstrate the utility of ENISI in guiding the design of wet-lab experiments.",

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AU - Alam, Maksudul

AU - Abedi, Vida

AU - Bassaganya-Riera, Josep

AU - Wendelsdorf, Katherine

AU - Bisset, Keith

AU - Deng, Xinwei

AU - Eubank, Stephen

AU - Hontecillas, Raquel

AU - Hoops, Stefan

AU - Marathe, Madhav

PY - 2016

Y1 - 2016

N2 - Agent-based modeling is a computational modeling framework for simulating the interactions of multiple diverse agents representing a complex system or phenomenon. The appoach has been successfully used to model a number of social, biological, and technological systems. In this chapter, we present ENteric Immunity SImulator (ENISI), an agent-based modeling framework for studying the inflammatory and regulatory immune pathways triggered by interactions among microbes and immune cells in the gut. In ENISI, individual cells move through simulated tissues and engage in context-dependent interactions with the other cells with which they are in contact. The scale of ENISI is unprecedented in this domain, with the ability to simulate 107-109 cells for 250 simulated days in 90min on a modest cluster. We describe the formal representation of the immune system as an agent-based model for modeling mucosal immune responses to gastrointestinal pathogens. We also describe performance and sensitivity analysis techniques and demonstrate the utility of ENISI in guiding the design of wet-lab experiments.

AB - Agent-based modeling is a computational modeling framework for simulating the interactions of multiple diverse agents representing a complex system or phenomenon. The appoach has been successfully used to model a number of social, biological, and technological systems. In this chapter, we present ENteric Immunity SImulator (ENISI), an agent-based modeling framework for studying the inflammatory and regulatory immune pathways triggered by interactions among microbes and immune cells in the gut. In ENISI, individual cells move through simulated tissues and engage in context-dependent interactions with the other cells with which they are in contact. The scale of ENISI is unprecedented in this domain, with the ability to simulate 107-109 cells for 250 simulated days in 90min on a modest cluster. We describe the formal representation of the immune system as an agent-based model for modeling mucosal immune responses to gastrointestinal pathogens. We also describe performance and sensitivity analysis techniques and demonstrate the utility of ENISI in guiding the design of wet-lab experiments.

KW - Agent-based models

KW - Data analytics

KW - Dynamic systems

KW - Graph

KW - High performance computing

KW - Sensitivity analysis

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SN - 9780128036976

SP - 79

EP - 111

BT - Computational Immunology

PB - Elsevier Inc.

ER -

Agent-Based Modeling and High Performance Computing

Abstract

Funding

Keywords

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