Chapter title |
Unraveling the Complex Regulatory Relationships Between Metabolism and Signal Transduction in Cancer
|
---|---|
Chapter number | 9 |
Book title |
Advances in Systems Biology
|
Published in |
Advances in experimental medicine and biology, January 2012
|
DOI | 10.1007/978-1-4419-7210-1_9 |
Pubmed ID | |
Book ISBNs |
978-1-4419-7209-5, 978-1-4419-7210-1
|
Authors |
Michelle L. Wynn, Sofia D. Merajver, Santiago Schnell, Wynn ML, Merajver SD, Schnell S, Wynn, Michelle L., Merajver, Sofia D., Schnell, Santiago |
Abstract |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 1 | 6% |
France | 1 | 6% |
Unknown | 14 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 5 | 31% |
Student > Doctoral Student | 2 | 13% |
Student > Ph. D. Student | 2 | 13% |
Professor | 2 | 13% |
Other | 1 | 6% |
Other | 2 | 13% |
Unknown | 2 | 13% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 7 | 44% |
Engineering | 3 | 19% |
Mathematics | 1 | 6% |
Biochemistry, Genetics and Molecular Biology | 1 | 6% |
Medicine and Dentistry | 1 | 6% |
Other | 1 | 6% |
Unknown | 2 | 13% |