Research News

Tuning Into Cellular Noise Reveals Network Secrets

It is a well-known fact among researchers who study cells that the internal genetic and biochemicals workings of any given cell are likely to differ at any one moment from the typical average measurement that scientists make when they study collections of cells. This “noise” has long been seen as a nuisance that confounds attempts to decipher the function of cellular networks. But to Brian Munsky, Ph.D., of the Los Alamos National Laboratory and colleague Mustafa Khammash, Ph.D., of the University of California, Santa Barbara (UCSB), this noise is beautiful, and they have shown that careful analysis of cellular noise can actually reveal far more than they hide about the workings of intercellular networks.

Munsky and Khammash, working with UCSB graduate student Brooke Trinh, used several computational models to mimic the observed behavior of individual cells as they responded to a variety of biochemical perturbations. When they processed the data with sophisticated computational tools and fed the results into their models, they found that by comparing the model’s results to averaged data they could identify key parameters of the perturbed networks after making measurements at a mere two time points.

The details of this study appear in a paper titled, “Listening to the noise: random fluctuations reveal gene network parameters.” An abstract of this paper is available at the journal’s Web site.
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