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Houghton, MI (OBBeC) - Researchers at Michigan Technological University are harnessing the computing muscle behind leading video games to understand the most intricate of real-life systems, according to a report from the institute.

The group, led by Roshan D'Souza, has supercharged agent-based modelling, a powerful but computationally massive forecasting technique, by using graphic processing units (GPUs), which drive the spectacular imagery beloved of video gamers.

In particular, the team aims to model complex biological systems, such as the human immune response to a tuberculosis bacterium.

Mikola Lysenko, a computer science student who wrote the software, demonstrates how a swarm of bright green immune cells surrounds and contains a yellow TB germ on his computer screen.

These busy specks look like 3D-animations from a PBS documentary, but they are actually virtual T-cells and macrophages—the visual reflection of millions of real-time calculations.

"I've been asked if we ran this on a supercomputer or if it's a movie," says D'Souza, an assistant professor of mechanical engineering–engineering mechanics.

He outlines that their model is several orders of magnitude faster than state-of-the art agent modelling toolkits. According to the researchers, however, this current effort is small potatoes.

"We can do it much bigger," says D'Souza. "This is nowhere near as complex as real life." Next, he hopes to model how a TB infection could spread from the lung to the patient's lymphatic system, blood and vital organs.

According to the report, the  TB model was developed by Dr. Denise Kirschner of the University of Michigan in Ann Arbor and given to D'Souza's team. The team then programmed it into a graphic processing unit.

Agent-based modelling hasn't replaced test tubes, she outlines, but it is providing a powerful new tool for medical research.

Computer models offer significant advantages. "You can create a mouse that's missing a gene and see how important that gene is," says Kirschner. "But with agent-based modelling, we can knock out two or three genes at once." In particular, agent-based modelling allows researchers to do something other methodologies can't: virtually test the human response to serious insults, such as injury and infection.

While agent-based modelling may never replace the laboratory entirely, it could reduce the number of dead-end experiments. "It really helps scientists focus their thinking," Kirschner said. "The limiting factor has been that these models take a long time to run, and [D'Souza's] method works very quickly and efficiently," she said.

What is agent-based modelling?
Agent-based modelling simulates the behaviours of complex systems. It can be used to predict the outcomes of anything from pandemics to the price of pork bellies. It is, as the name suggests, based on individual agents: e.g., sick people and well people, predators and prey, etc. It applies rules that govern how those agents behave under various conditions, sets them loose, and tracks how the system changes over time. The outcomes are unpredictable and can be as surprising as real life.

Agent-based modelling has been around since the 1950s, but the process has always been handicapped by a shortage of computing power. Until recently, the only way to run large models quickly was on multi-million-dollar supercomputers, a costly proposition.

D'Souza's team side-stepped the problem by using GPUs, which can run models with tens of millions of agents with blazing speed.

"With a $1,400 desktop, we can beat a computing cluster," says D'Souza. "We are effectively democratizing supercomputing and putting these powerful tools into the hands of any researcher. Every time I present this research, I make it a point to thank the millions of video gamers who have inadvertently made this possible."