Moment by moment, a movie captures the action as a group of immune cells scrambles to counter an invasion of tuberculosis bacteria. Rushing to the site of infected lung tissue, the cells build a complex sphere of active immune cells, dead immune cells, lung tissue, and trapped bacteria. Remarkably, no lung tissue or bacterium was harmed in the making of this film.
Instead, each immune cell is a computer simulation, programmed to fight virtual tuberculosis bacteria on a square of simulated lung tissue. In their computer-generated environment, these warrior cells spontaneously build a structure similar to the granulomas that medical researchers have noted in human lungs fighting tuberculosis.
The simulation, created by Denise Kirschner of the University of Michigan in Ann Arbor, is an example of an emerging technique called agent-based modeling. This new tool in the world of medical research relies on computing power instead of tissues and test tubes. A growing cadre of researchers, including Kirschner, predicts that agent-based modeling will usher in a broadened understanding of complex interactions within the human body.
The agents in the models are individual players--immune cells in the tuberculosis example. Each player is programmed with rules that govern its behavior. Computer-savvy researchers then set the agents free to cooperate with, compete with, or kill each other. Meanwhile, the agents must navigate the surrounding environment, whose properties can vary over space and time.
Scientists can manipulate disease progression within the models by changing the agents or their environment and then watching what happens. As opposed to traditional, biologically based in vivo or in vitro experiments, these computer trials are dubbed "in silico." The results can suggest biological experiments to test the models' findings and may eventually lead to new medical treatments.
Even simple rules assigned to agents can give rise to surprisingly complex behaviors. When many independent agents interact, they create phenomena--such as the granulomas--that can't necessarily be predicted by breaking down the system into its …