Cancer Systems Biology

Systems biology is predicated on the use of model-based, genome-anchored methodologies to elucidate biological phenotypes and mechanisms. Models belong to one of two classes: first-principle, mathematical models aimed at representing the underlying biology that determines cell behavior—such as the interaction between two proteins, based on their structural and electrostatic properties—and abstract models, typically inferred by machine learning approaches, whose goal is to make accurate, biologically-relevant predictions without necessarily requiring biological interpretability. Having led in the generation of the large-scale, multi-omics molecular profile data necessary for model creation and interrogation, the cancer research community has played a prominent role in the development of the field. In return, systems approaches have helped elucidate cancer-related drivers and cellular mechanisms that had proven elusive using a gene-by-gene approach, leading to translational applications—ranging from outcome and drug sensitivity predictions to elucidating drug mechanism of action and cell adaptation— that are starting to impact clinical practice. Critically, these methodologies enhance our understanding of normal cell physiology as well as its dysregulation in non-cancer related diseases, from diabetes to neurodegenerative and autoimmune syndromes.