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'Fuzzy' Modeling Approach Sharpens View of Cellular Decision-Making
Adapted from an MIT news release External link
Posted April 16, 2009

Using "fuzzy logic," researchers are bringing the inner workings of cells into focus. This clearer picture could help us understand human diseases and predict the effects of potential treatments.

Our cells are constantly bombarded with messages from the outside world—hormones and other chemicals that tell them to grow, migrate, die or just be. Inside the cell, complex signaling networks interpret these messages and make life-and-death decisions.

But how exactly do they make these tough decisions?

To find out, a team of biological engineers at the Massachusetts Institute of Technology turned to a technique developed in the 1960s and applied in auto-focusing cameras and cruise control in cars. It's called fuzzy logic.

The fuzzy logic model produces diagrams like this one that allow scientists to visualize what is happening inside a cell. Credit: Doug Lauffenburger
The fuzzy logic model produces diagrams like this one that allow scientists to visualize what is happening inside a cell. Credit: Doug Lauffenburger
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Despite its name, fuzzy logic can take inexact inputs and produce accurate predictions. It mimics the way humans make decisions. Your choice to eat lunch may depend on the time of day, the availability of food and your level of hunger. You probably don't realize it, but all this information is integrated to come up with your final decision.

The fuzzy logic model for cell signaling works in much the same way. Each part of the network has its own set of rules that determine how it responds to a stimulus, like a hormone. Adding up all of the stimuli and responses leads to an overall outcome, such as cell death, division or migration.

The model also generates a graphical representation of each step along the way, allowing scientists to visualize what is happening inside the cell. With fuzzy logic models, "you can actually see the drawing and say, 'Aha, I see what this enzyme is doing,'" says Doug Lauffenburger, who led the research team.

In this case, the model revealed some previously unknown interactions in a pathway regulating programmed cell death. The pathway, called MK2, is generally believed to promote cell death, but the model showed that inhibiting it can actually favor cell death—that's because the pathway may also control another signal that's pro-survival.

Without the fuzzy logic model, "you wouldn't have found that connection and would not be able to properly understand what an anti-MK2 drug might do," says Lauffenburger.

This is the first time that fuzzy logic has been used to model cell biochemistry, and the approach should be applicable to any kind of cell signaling pathway. The researchers say it also should be useful in identifying potential new targets for drugs against cancer, inflammatory diseases and infectious diseases.

According to Jim Anderson of the National Institute of General Medical Sciences, the work underscores a much more fundamental idea. He says, "As biomedical science tackles the really hard problems posed by complex systems, the analytical tools must become increasingly sophisticated. Such tools frequently are found in unrelated disciplines, as illustrated by this work."

Learn about related research

This page last reviewed on April 22, 2011