Genes and gene products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic- and signal transduction networks. Genetic, biochemical and molecular biology techniques have been used for decades to identify biological interactions; newly developed high-throughput methods now allow for the construction of genome-level interaction maps. In parallel, high-throughput expression data paired with computational algorithms can be used to infer networks of interactions and causal relationships capable of producing the observed experimental data. Graph-theoretical measures and network models are more and more frequently used to discern functional and evolutionary constraints in the organisation of biological networks. Perhaps most importantly, the combination of interaction and expression information allows the formulation of quantitative and predictive dynamic models. Some of the dominant experimental and computational methods used for the reconstruction or inference of cellular networks are reviewed, also the biological insights that have been obtained from graph-theoretical analysis of these networks, and the extension of static networks into various dynamic models capable of providing a new layer of insight into the functioning of cellular systems is discussed.