Transcriptome to Reactome Deterministic Modeling: Validation of in Silico Simulations of Transforming Growth Factor-β1 Signaling in MG63 Osteosarcoma Cells, TTR Deterministic Modeling

  • Clyde F. Phelix
  • Bethaney Watson
  • Richard G. LeBaron
  • Greg Villareal
  • Dawnlee Roberson
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 145)


Integrated Systems Biology was used to study bone cancer via an iterative process of in vitro testing for validation of an in silico computer simulation where the transcriptome was used to derive the parameters of a kinetic model. A computer simulation model of the transforming growth factor-beta (TGF-β1) signaling pathway was obtained from Reactome®. The transcriptome of MG-63 cells was accessed from NCBI GEO GSE11414. With this method the model is not trained to match the biological system. The in vitro study on osteosarcoma (MG-63) cells was used to compare with the results from the computer simulation. MG-63 cells were grown in culture and exposed to TGF-β1 to identify differences in expression of a target-gene, TGF-β -Induced 68kDa protein (TGFBI), at serial time intervals. Real-time PCR was used to measure TGFBI mRNA levels and the temporal profile was identical with that predicted by the in silico model. A sensitivities test was performed through the in silico model and a candidate target for gene-knock-down in the TGF-β signaling pathway, Smad3, was identified. An 80% reduction of this reactant in the model attenuated TGFBI expression by 64%, an effect that matched such knockdown of Smad3, in vitro, for other target genes reported in the literature. The assumption that the transcriptome drives the reactome is validated and substantiates a novel method for deriving parameters for kinetic deterministic models of biological systems.


kinetic model gene expression profile parameter estimation sensitivities analysis 


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Clyde F. Phelix
    • 1
  • Bethaney Watson
    • 1
  • Richard G. LeBaron
    • 1
  • Greg Villareal
    • 2
  • Dawnlee Roberson
    • 3
  1. 1.University of Texas at San AntonioSan AntonioUSA
  2. 2.CFPAL Biomedical ConsultantsSan AntonioUSA
  3. 3.AL Phahelix Biometrics IncSan AntonioUSA

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