Mathematical models based on biochemical reaction mechanisms can be a powerful complement to experimental investigations of cell signaling networks. In principle, such models have the potential to find the behaviors that result from well-understood component interactions and their measurable properties, such as concentrations and rate constants. As cancer results from the acquisition of mutations that alter the expression level and/or the biochemistry of proteins encoded by mutated genes, mathematical models of cell signaling networks would also seem to have the potential to predict how these changes alter cell signaling to produce a cancer phenotype. Ras is commonly found in cancer and has been extensively characterized at the level of detail needed to develop such models. Here, we consider how biochemical mechanism-based models have been used to study mutant Ras signaling. These models demonstrate that it is clearly possible to use observable properties of individual reactions to predict how the entire system behaves to produce the high levels of signal that drive the cancer phenotype. These models also demonstrate differences in how models are developed and studied. Their evaluation suggests which approaches are most promising for future work.
Copyright © 2011 John Wiley & Sons, Inc.