A longstanding barrier to progress in ageing research has been the coexistence of multiple, seemingly competing hypotheses about causal mechanisms. Each mechanism tends to be partially supported by data indicating that it has a role in the overall cellular and molecular pathways underlying the ageing process. However, the magnitude of this role is usually modest. The systems biology approach combines (i) data-driven modeling, often using the large volumes of data generated by functional genomics technologies, and (ii) hypothesis-driven experimental studies to investigate causal pathways and identify their parameter values in an unusually quantitative manner, which enables the contributions of individual mechanisms and their interactions to be better understood and it allows for the design of experiments explicitly designed to test the complex predictions arising from such models. Examples of the success of the systems biology approach in unraveling the complexity of ageing can be seen in recent studies on cell replicative senescence, mitochondrial dysfunction and breakdown in protein homeostasis. An important challenge also exists in connecting the network of (random) damage-driven proximate mechanisms of ageing with the higher level (genetically specified) signalling pathways that influence longevity. This connection is informed by actions of natural selection on the determinants of ageing and longevity.