  Cancers remain the lead cause of disease-related, pediatric death in North
America. The emerging field of complex systems has redefined cancer networks as
a computational system with intractable algorithmic complexity. Herein, a tumor
and its heterogeneous phenotypes are discussed as dynamical systems having
multiple, strange attractors. Machine learning, network science and algorithmic
information dynamics are discussed as current tools for cancer network
reconstruction. Deep Learning architectures and computational fluid models are
proposed for better forecasting gene expression patterns in cancer ecosystems.
Cancer cell decision-making is investigated within the framework of complex
systems and complexity theory.
