Unraveling the Complex Regulatory Relationships Between Metabolism and Signal Transduction in Cancer

  • Michelle L. Wynn
  • Sofia D. Merajver
  • Santiago SchnellEmail author
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)


Cancer cells exhibit an altered metabolic phenotype, known as the Warburg effect, which is characterized by high rates of glucose uptake and glycolysis, even under aerobic conditions. The Warburg effect appears to be an intrinsic component of most cancers and there is evidence linking cancer progression to mutations, translocations, and alternative splicing of genes that directly code for or have downstream effects on key metabolic enzymes. Many of the same signaling pathways are routinely dysregulated in cancer and a number of important oncogenic signaling pathways play important regulatory roles in central carbon metabolism. Unraveling the complex regulatory relationship between cancer metabolism and signaling requires the application of systems biology approaches. Here we discuss computational approaches for modeling protein signal transduction and metabolism as well as how the regulatory relationship between these two important cellular processes can be combined into hybrid models.


Boolean Network Boolean Model Central Carbon Metabolism Protein Signal Transduction Cancer Metabolism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was partially supported by the University of Michigan Center for Computational Medicine & Bioinformatics Pilot Grant 2010 and the National Science Foundation under Grant No. DMS-1135663. MLW acknowledges support from the Rackham Merit Fellowship, NIH T32 CA140044, and the Breast Cancer Research Foundation. SDM acknowledges support from the Burroughs Wellcome Fund, Breast Cancer Research Foundation, the Avon Foundation and NIH CA77612. SS acknowledges support from NIDDK R25 DK088752.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Michelle L. Wynn
    • 1
  • Sofia D. Merajver
    • 2
  • Santiago Schnell
    • 3
    Email author
  1. 1.Center for Computational Medicine and BioinformaticsUniversity of Michigan Medical SchoolAnn ArborUSA
  2. 2.Department of Internal Medicine and Center for Computational Medicine and BioinformaticsUniversity of Michigan Medical SchoolAnn ArborUSA
  3. 3.Department of Molecular and Integrative Physiology, Center for Computational Medicine and Bioinformatics and Brehm Center for Diabetes ResearchUniversity of Michigan Medical SchoolAnn ArborUSA

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