Engineers have used CAD software for years in structural design. Just as importantly, they have been able to use computational analysis to predict structural behavior in “test environments”, such as earthquakes, windstorms etc. Under a process called model breakpoint analysis, the computer designed structure is exposed to these varying processes until a critical structural flaw is identified.
Could this work with cell models? For years, Michael Yaffe and colleagues at MIT have been working to develop computerized simulations of cell signaling processes, but now they have turned their attention on trying to “break it”. Borrowing from the engineering approaches to detect structural flaws in computational models, they began altering key biological parameters in their computational cell model to see if they could detect critical “breakpoint” events that resulted in cell death.
Could these key events result in the production of chemotherapeutic medications that target the identified weaknesses? I’ll let you peruse the article summary before directing you to the MIT press release which appears to resemble English much more closely (and they say medical doctors speak over people’s heads!)
From the Oct. 17 issue of the journal Cell:
Signaling networks respond to diverse stimuli, but how the state of the signaling network is relayed to downstream cellular responses is unclear. We modeled how incremental activation of signaling molecules is transmitted to control apoptosis as a function of signal strength and dynamic range. A linear relationship between signal input and response output, with the dynamic range of signaling molecules uniformly distributed across activation states, most accurately predicted cellular responses. When nonlinearized signals with compressed dynamic range relay network activation to apoptosis, we observe catastrophic, stimulus-specific prediction failures. We develop a general computational technique, “model-breakpoint analysis,” to analyze the mechanism of these failures, identifying new time- and stimulus-specific roles for Akt, ERK, and MK2 kinase activity in apoptosis, which were experimentally verified. Dynamic range is rarely measured in signal-transduction studies, but our experiments using model-breakpoint analysis suggest it may be a greater determinant of cell fate than measured signal strength.
And now the MIT press release:
After spending years developing a computational model to help illuminate cell signaling pathways, a team of MIT researchers decided to see what would happen if they “broke” the model. A couple of years ago, MIT faculty member Michael Yaffe and colleagues reported a data-driven computational model that allows them to simultaneously investigate the relationships between several cell signaling pathways, which control the cell’s response to inflammation, growth factors, DNA damage and other events. Such a model can help researchers figure out how cells will respond to growth factors and treatments like chemotherapy, allowing them to potentially tailor treatments to individual patients.
In the Cell paper, the team took a new approach to wring more information out of their model: They looked at what happens in cells under conditions where the model fails catastrophically. This approach, which the authors call “model breakpoint analysis,” is an extension of more traditional failure analysis methods commonly used by engineers to figure out what design changes would make a bridge fall down or an engine fail.
To “break” the computer model, the researchers entered increasingly implausible inputs to the model until it no longer correctly predicted cell fate. To their surprise, they found that the model remained accurate for a long time. “As the data we used to build the model got progressively worse and worse — more and more biologically inaccurate — the model would work fine, and then when you got to a certain threshold — the breakpoint — the model suddenly wouldn’t predict anymore,” said Yaffe, who is affiliated with MIT’s David H. Koch Institute for Integrative Cancer Research, the Broad Institute of MIT and Harvard, and Beth Israel Deaconess Medical Center.
“By going back and looking at what happened in the model when the predictions failed, we discovered a surprising amount of new biology that was actually happening in the living cell,” Yaffe said. One significant, unexpected finding is the observation that both overactive and underactive mutations within a particular gene, such as those found in cancer, reduce cell death compared to the normal gene. This suggests that normal cells are poised to die whenever there is trouble, but perhaps not tumor cells.
That means the dynamic range of cell signaling — the spread between the highest and lowest levels of a biological signal, like the range of volumes you can hear on your stereo — may be a greater determinant of what cells do than the absolute level of a particular signal, said Yaffe.
The computer modeling approach offers the chance to learn about biological phenomena that might take thousands of hours in the laboratory to uncover, according to Yaffe.
“In addition, rather than looking at one pathway in the cell in isolation, we could look at five pathways or eight pathways simultaneously,” he said. “It also reinforces how engineering ideas can really illuminate biological mechanisms.”
Abstract: Cytokine-Induced Signaling Networks Prioritize Dynamic Range over Signal Strength; Cell, Vol 135, 343-354, 17 October 2008
MIT press release: Computer model reveals cells’ inner workings
