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Abstract
This Podcast features an interview with Kevin Janes, senior author of a Research Article that appears in the 7 June 2016 issue of Science Signaling, about a statistical modeling method that can extract useful information from complex data sets. Cells exist in very complex environments. They are constantly exposed to growth factors, hormones, nutrients, and many other factors that influence cellular behavior. When cells integrate information from multiple stimuli, the resulting output does not necessarily reflect a simple additive effect of the responses to each individual stimulus. Chitforoushzadeh et al. employed a statistical modeling approach that maintained the multidimensional nature of the data to analyze the responses of colonic epithelial cells to various combinations of the proinflammatory cytokine TNF, the growth factor EGF, and insulin. As the model predicted, experiments confirmed that insulin suppressed TNF-induced proinflammatory signaling through a mechanism that involved the transcription factor GATA6.
