Mathematical models on cancer activated gene regulatory networks

Genomic activations in cancer are a mixture of driving events that promote cancer progression and passenger events that represent a large fraction of random somatic alterations. Such activations can be represented as graph structures which are often large in scale. The problem for distinguishing the driver alterations that produce dramatic effects on such activations in biomolecular networks is a critical issue in cancer research since an effective therapies against cancer should target dominant driver mutations that promote cell migration and invasion into malignant derivatives. This book reflects our recent advances in identifications of cancer-related genetic alterations based on the analysis of genetic data, using statistical graph estimations and large scale optimization techniques. It is aimed at graduate students and researchers as well as cancer specialists, and assumes interests in a reasonable connection between molecular system biology and graphical modeling.