title: Multi-pathway network analysis of mammalian epithelial cell responses in inflammatory environments.
abstract: Inflammation is a key physiological response to infection and injury and, although usually beneficial, it can also be damaging to the host. The liver is a prototypical example in this regard because inflammation helps to resolve liver injury, but it also underlies the aetiology of pathologies such as fibrosis and hepatocellular carcinoma. Liver cells sense their environment, including the inflammatory environment, through the activities of receptor-mediated signal transduction pathways. These pathways are organized in a complex interconnected network, and it is becoming increasingly recognized that cellular adaptations result from the quantitative integration of multi-pathway network activities, rather than isolated pathways causing particular phenotypes. Therefore comprehending liver cell signalling in inflammation requires a scientific approach that is appropriate for studying complex networks. In the present paper, we review our application of systems analyses of liver cell signalling in response to inflammatory environments. Our studies feature broad measurements of cell signalling and phenotypes in response to numerous experimental perturbations reflective of inflammatory environments, the data from which are analysed using Boolean and fuzzy logic models and regression-based methods in order to quantitatively relate the phenotypic responses to cell signalling network states. Our principal biological insight from these studies is that hepatocellular carcinoma cells feature uncoupled inflammatory and growth factor signalling, which may underlie their immune evasion and hyperproliferative properties.
authors.affiliation: Department of Biological Engineering and the Center for Cellular Decision Processes, Massachusetts Institute of Technology, 77 Mass Ave., Cambridge, MA 02139, USA.
authors.forename: David C
authors.initials: DC
authors.lastname: Clarke
authors.forename: Douglas A
authors.initials: DA
authors.lastname: Lauffenburger
mesh_terms: D000818:Animals; D003198:Computer Simulation; D022781:Hepatocytes / Q000378:metabolism* / Q000473:pathology; D006801:Humans; D007249:Inflammation / Q000378:metabolism* / Q000473:pathology; D047908:Intracellular Signaling Peptides and Proteins / Q000378:metabolism; D008099:Liver / Q000378:metabolism / Q000473:pathology; D008954:Models, Biological; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D012044:Regression Analysis; D015398:Signal Transduction
chemical_list: D047908:Intracellular Signaling Peptides and Proteins