Identifying a Cell’s Weakest Link

To understand why bridges collapse or computers fail, engineers might create models of these systems and push them beyond their limits. Now, computational biologists are using a similar approach to understand the causes of cell death. By driving their model of the cell beyond experimentally observed values of certain important cellular ingredients, they push it to the “breaking point”—uncovering the weakest links. The process revealed some new biological roles for several key signaling molecules—the kinases ERK, Akt, and MK2.

Breakpoint model analysis pushes cellular ingredients beyond their normal ranges to see which ones are critical to a particular cellular process. Here we see fluorescent proteins highlighting the subcellular loca- tion of several different key signaling mole- cules (phosphoinositide-binding domains), which function together with lipid and pro- tein kinases and phosphoserine/threonine- binding domains, to control a wide variety of cellular events. These are the kinds of molecular interactions that could be studied using breakpoint model analysis. Courtesy of Seth J. Field and Michael Yaffe.

“It showed us things that, in retrospect, we couldn’t see looking by inspection of the original model,” says co-author Michael Yaffe, PhD, associate professor of biology and biological engineering at the Massachusetts Institute of Technology (MIT). The work was published in the October 17, 2008 issue of Cell.

 

The mechanisms by which proteins influence cytokine-induced apoptosis, or cell death, are poorly understood. So Yaffe and colleagues Kevin Janes, PhD, a recent MIT graduate, and H. Christian Reinhardt, PhD, a postdoctoral associate at MIT, built a model of the cell using carefully collected data. Included in the model were nearly 8,000 measurements of protein signals in response to combinations of three cytokines that help dictate the fates of cells: tumor necrosis factor (TNF), known as the “death stimulus,” and epidermal growth factor (EGF) and insulin, known as “survival stimuli.”

 

The researchers then manipulated the model to drive the activity levels of the proteins outside of their experimentally observed ranges. When the model could no longer computationally fit one of the signal variables, it would stop making predictions. This “breaking point” highlighted the protein that caused the failure. Thus, the technique acts as a sort of high-throughput screen, revealing new hypotheses about proteins previously thought to have well-defined roles within the cell. The team then verified these hypotheses experimentally, leading to surprising new insights about how the signaling proteins communicate. “Signaling networks are so complicated right now that common sense doesn’t always hold true,” Yaffe says.

 

“The thing that makes me really stop and pay attention is the methodology, which I found of special note,” says Raphael Levine, PhD, distinguished professor of chemistry at the University of California, Los Angeles. “Instead of trying to see if the model can predict something new, they tried to drive it to say something which they know it shouldn’t say. As a result, they were successful in finding some new biology."
 



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