Argonne uses new computational and experimental tools to detect engineered biological systems and monitor their potential to spread disease. At Argonne, this means employing agent-based models and large-scale simulations to understand how pathogens might spread. This approach predicts the behavior of individual ​“agents” – representing people, households, organizations, etc. – and how they interact. With agent-based modeling, researchers can explore how entire populations may evolve and identify tipping points that affect their fate in countless scenarios involving bacteria, viruses and the environment.

Focusing on National Security

Meaningful detection and surveillance of naturally or genetically engineered pathogens requires comprehensive knowledge of systems at the sub-cellular level. Gene-editing systems, such as CRISPR-Cas, occur naturally and offer scientists important insights required to model microbial systems for global impact.

Argonne’s deep expertise in high performance computing and machine learning helps researchers understand the evolutionary and mechanistic biology. With this knowledge, experts can identify vulnerabilities and provide risk assessments related to naturally occurring gene-editing processes in bacteria. With this data, policy experts and government officials can take informed action to safeguard public health and well-being.

Collaborating for Results

Argonne’s Rapid Annotation using Subsystem Technology (RAST) is the world’s most-cited bacterial genome analysis and annotation system in the open scientific literature. Argonne also is a partner with the University of Chicago in the Pathosystems Resource Integration Center (PATRIC). PATRIC is an information system designed to support microbial genomic analysis and the biomedical research community’s work on bacterial infectious diseases. Argonne also hosts the database used throughout the world to annotate bacterial metagenomic data. Such collaboration establishes Argonne as a recognized world leader in the application of agent-based modeling to large-scale simulations.

Achieving Success

Argonne researchers developed an agent-based computer model for Chicago, the Chicago Social Interaction Model (chiSIM). It simulates the city’s population and the daily activities and contacts of the individual ​“agents” at varied locations. During a simulated day, agents move from place to place, hour by hour, interacting with other agents, thus potentially spreading disease. This agent-based modeling, coupled with the capabilities of the MIRA supercomputer at the Argonne Leadership Computing Facility, form a rare combination of human and computational resources.

When the Ebola virus swept through West Africa in 2014, Argonne researchers joined DOE’s Ebola Task Force to halt the international health crisis. The Argonne team tuned their extensive computer models and adapted the models for Ebola. Argonne’s approach provides estimates regarding the healthcare resources required – space, staff and how to dispose of medical waste and informs ways to respond to – and suppress – the spread of various diseases.