Signaling network state predicts Twist-mediated effects on breast cell migration across diverse growth factor contexts

• Main Authors: Kim, Hyung-Do (Contributor), Meyer, Aaron Samuel (Contributor), Wagner, Joel Patrick (Contributor), Wells, Alan (Author), Gertler, Frank (Contributor), Hughes-Alford, Shannon Kay (Author), Lauffenburger, Douglas A (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Biology (Contributor), Koch Institute for Integrative Cancer Research at MIT (Contributor), Lauffenburger, Douglas A. (Contributor), Alford, Shannon K. (Contributor)
• Format: Article
• Language: English
• Published: American Society of Biochemistry and Molecular Biology, 2012-01-04T19:53:42Z.
• Subjects: Article
• Online Access: Get fulltext

 Epithelial-mesenchymal transition (EMT), whether in developmental morphogenesis or malignant transformation, prominently involves modified cell motility behavior. Although major advances have transpired in understanding the molecular pathways regulating the process of EMT induction per se by certain environmental stimuli, an important outstanding question is how the activities of signaling pathways governing motility yield the diverse movement behaviors characteristic of pre-induction versus postinduction states across a broad landscape of growth factor contexts. For the particular case of EMT induction in human mammary cells by ectopic expression of the transcription factor Twist, we found the migration responses to a panel of growth factors (EGF, HRG, IGF, HGF) dramatically disparate between confluent pre-Twist epithelial cells and sparsely distributed post-Twist mesenchymal cells-but that a computational model quantitatively integrating multiple key signaling node activities could nonetheless account for this full range of behavior. Moreover, motility in both conditions was successfully predicted a priori for an additional growth factor (PDGF) treatment. Although this signaling network state model could comprehend motility behavior globally, modulation of the network interactions underlying the altered pathway activities was identified by ascertaining differences in quantitative topological influences among the nodes between the two conditions.