  We present a general computational theory of cancer and its developmental
dynamics. The theory is based on a theory of the architecture and function of
developmental control networks which guide the formation of multicellular
organisms. Cancer networks are special cases of developmental control networks.
Cancer results from transformations of normal developmental networks. Our
theory generates a natural classification of all possible cancers based on
their network architecture. Each cancer network has a unique topology and
semantics and developmental dynamics that result in distinct clinical tumor
phenotypes. We apply this new theory with a series of proof of concept cases
for all the basic cancer types. These cases have been computationally modeled,
their behavior simulated and mathematically described using a multicellular
systems biology approach. There are fascinating correspondences between the
dynamic developmental phenotype of computationally modeled {\em in silico}
cancers and natural {\em in vivo} cancers. The theory lays the foundation for a
new research paradigm for understanding and investigating cancer. The theory of
cancer networks implies that new diagnostic methods and new treatments to cure
cancer will become possible.
