title: Chemical kinetic mechanistic models to investigate cancer biology and impact cancer medicine.
abstract: Traditional experimental biology has provided a mechanistic understanding of cancer in which the malignancy develops through the acquisition of mutations that disrupt cellular processes. Several drugs developed to target such mutations have now demonstrated clinical value. These advances are unequivocal testaments to the value of traditional cellular and molecular biology. However, several features of cancer may limit the pace of progress that can be made with established experimental approaches alone. The mutated genes (and resultant mutant proteins) function within large biochemical networks. Biochemical networks typically have a large number of component molecules and are characterized by a large number of quantitative properties. Responses to a stimulus or perturbation are typically nonlinear and can display qualitative changes that depend upon the specific values of variable system properties. Features such as these can complicate the interpretation of experimental data and the formulation of logical hypotheses that drive further research. Mathematical models based upon the molecular reactions that define these networks combined with computational studies have the potential to deal with these obstacles and to enable currently available information to be more completely utilized. Many of the pressing problems in cancer biology and cancer medicine may benefit from a mathematical treatment. As work in this area advances, one can envision a future where such models may meaningfully contribute to the clinical management of cancer patients.
authors: The Translational Genomics Research Institute, Clinical Translational Research Division, 13208 E Shea Blvd, Scottsdale, AZ 85258, USA. estites@tgen.org
authors: Edward C
authors: EC
authors: Stites
mesh_terms: D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D009154:Mutation; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D015398:Signal Transduction; D049490:Systems Biology