Nonparametric simulation of signal transduction networks with semi-synchronized update

Nassiri, I ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1371/journal.pone.0039643
  3. Publisher: 2012
  4. Abstract:
  5. Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process
  6. Keywords:
  7. Epidermal growth factor receptor ; Immunoglobulin enhancer binding protein ; Insulin receptor ; Mitogen activated protein kinase 1 ; Phosphoprotein ; Protein kinase B ; S6 kinase ; Somatomedin C receptor ; Animal cell ; Breast cancer ; Cancer cell culture ; Controlled study ; Downstream processing ; Hepatoblastoma ; Human ; Human cell ; Intracellular signaling ; Liver cell ; Mathematical computing ; Molecular dynamics ; Mouse ; Nonhuman ; Nonparametric test ; Process model ; Protein function ; Protein interaction ; Signal transduction ; Algorithms ; Cell Line, Tumor ; Cluster Analysis ; Computer Simulation ; Enzyme-Linked Immunosorbent Assay ; Gene Expression Regulation, Neoplastic ; Hepatocytes ; Humans ; Inflammation ; Kinetics ; Ligands ; Models, Statistical ; Receptor, Epidermal Growth Factor ; Statistics, Nonparametric
  8. Source: PLoS ONE ; Volume 7, Issue 6 , 2012 ; 19326203 (ISSN)
  9. URL: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0039643