Information theory helps predict biological signaling
October 27, 2011
Andre Levchenko, associate professor of BME, and his lab have described techniques that enable use of information theory in analysis of biological decision making, particularly in networks of signaling pathways. The first-of-its-kind study, published on September 15 in Science combines live-cell experiments and math to convert the inner workings of the cell decision-making process into a universal mathematical language, allowing information processing in cells to be compared with the computing power of machines. This can shed a completely new light on many of the current problems in the analysis of cell function and tissue development.
“Each cell interprets a signal from the environment in a different way, but if many cells join together, forming a common response, the result can eliminate the differences in the signal interpretation while emphasizing the common response features,” says Levchenko. The research demonstrates why it’s advantageous for cells to cooperate to overcome their meager individual decision-making abilities by forming multicellular organisms.
The researchers calculated a single cell’s response to be 0.92 bits of information, allowing for two possible decisions. When they examined clusters of cells and compiled this data into their equation, they found that clusters of as few as 14 cells could produce 1.8 bits of information, corresponding to somewhere from 3 to 4 different potential decisions for the cluster.
The fact that combinations of cells can make more decisions suggests why being multicellular is such a good thing in the animal world and why cells can sometimes achieve so much more if they are working together than separately, says Levchenko.
The first author on the study, Raymond Cheong, was responsible for much of the experimental and theoretical analysis. Other researchers involved included Alex Rhee and Chiaochun Joanne Wang of Johns Hopkins and Ilya Nemenman of Emory University.
The study was supported by the National Institutes of Health, the Medical Scientist Training Program at Johns Hopkins and the Los Alamos National Laboratory Directed Research and Development Program.