title: Convergence of logic of cellular regulation in different premalignant cells by an information theoretic approach.
abstract: BACKGROUND
  Surprisal analysis is a thermodynamic-like molecular level approach that identifies biological constraints that prevents the entropy from reaching its maximum. To examine the significance of altered gene expression levels in tumorigenesis we apply surprisal analysis to the WI-38 model through its precancerous states. The constraints identified by the analysis are transcription patterns underlying the process of transformation. Each pattern highlights the role of a group of genes that act coherently to define a transformed phenotype.
  
  
  RESULTS
  We identify a major transcription pattern that represents a contraction of signaling networks accompanied by induction of cellular proliferation and protein metabolism, which is essential for full transformation. In addition, a more minor, "tumor signature" transcription pattern completes the transformation process. The variation with time of the importance of each transcription pattern is determined. Midway through the transformation, at the stage when cells switch from slow to fast growth rate, the major transcription pattern undergoes a total inversion of its weight while the more minor pattern does not contribute before that stage.
  
  
  CONCLUSIONS
  A similar network reorganization occurs in two very different cellular transformation models: WI-38 and the cervical cancer HF1 models. Our results suggest that despite differences in a list of transcripts expressed in different cancer models the rationale of the network reorganization remains essentially the same.
authors.affiliation: Unit of Cellular Signaling, Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.
authors.forename: Nataly
authors.initials: N
authors.lastname: Kravchenko-Balasha
authors.forename: F
authors.initials: F
authors.lastname: Remacle
authors.forename: Ayelet
authors.initials: A
authors.lastname: Gross
authors.forename: Varda
authors.initials: V
authors.lastname: Rotter
authors.forename: Alexander
authors.initials: A
authors.lastname: Levitzki
authors.forename: R D
authors.initials: RD
authors.lastname: Levine
mesh_terms: D002460:Cell Line; D002471:Cell Transformation, Neoplastic / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007257:Information Theory; D011230:Precancerous Conditions / Q000378:metabolism*; D016159:Tumor Suppressor Protein p53 / Q000378:metabolism; D018631:ras Proteins / Q000378:metabolism
chemical_list: D016159:Tumor Suppressor Protein p53; D018631:ras Proteins