Cancer systems biology integrates experimental models, data analysis, and
dynamic network modeling to elucidate the complex mechanisms underlying cancer
progression. This chapter outlines the essential requirements for experimental models,
emphasizing the need for well-characterized cancer subtypes and high-quality mouse
models that mimic clinical outcomes. It discusses various approaches to constructing
cancer gene networks, including inference from genome-wide datasets, extension of
protein interaction networks, and integration of high-throughput data with literature. The
chapter also highlights advancements in bioinformatics, such as pattern recognition and
machine learning, and the evolution of network visualization from static to dynamic
models. Finally, it examines network analysis techniques for understanding biological
systems and applying dynamic network modeling to decipher information processing in
cancer cells. Data quality and model development challenges are noted, with a call for
enhanced training in network-based thinking to further cancer research.
Keywords: Bioinformatics, Cancer networks, Data integration, Dynamic modeling, Network visualization.
