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Integration of Genetic, Proteomic, and Metabolic Approaches in Tumor Cell Metabolism

  • Leslie C. Costello
  • Renty B. Franklin
Chapter

Abstract

There now exists a resurgence of interest in and research into the role of altered cellular intermediary metabolism in the development and progression of cancer and other disease processes. The recent developments in molecular technology, molecular biology, molecular genetics, and proteomics provide new tools to investigate the involvement of the genetic and molecular factors that regulate the adaptation of the metabolism of malignant cells to meet the synthetic and bioenergetic requirements of the malignant process. Such didactic and technological capabilities that did not exist during the preceding generations of research in intermediary metabolism must be integrated with the biochemical/enzymological methods and principles of cellular enzyme activities and metabolic pathways. This requires that the contemporary and future investigators integrate the traditional cellular metabolic principles and methods with the molecular technological capabilities and didactic information to study the role of altered intermediary in malignancy. A guiding axiom is that “Genetic transformations and proteomic alterations will have little relevancy to tumor metabolism and other disease processes if the genetic/proteomic alterations are not manifested in altered and impaired cellular and metabolic function.” In this chapter we discuss some important principles of cellular metabolism and the approaches employed to determine the metabolic adaptations involved in malignancy. We integrate the areas of cellular intermediary metabolism with molecular genetics, proteomics, and metabolomics to provide the basis for elucidation of the genetic/molecular/metabolic factors in the development and progression of cancer.

Keywords

Malignant Cell Electron Transport System Specific Enzyme Activity Molecular Technology Intermediary 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.

Notes

Acknowledgments

The cited studies of LCC and RBF described in this review were supported in part by NIH grants CA71207, CA21097, CA79903, and CA93443.

References

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Copyright information

© Springer Science + Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Physiology and Endocrinology, Greenebaum Cancer CenterUniversity of MarylandBaltimoreUSA

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