Complex networks: From graph theory to biology

The aim of this text is to show the central role played by networks in complex system science. A remarkable feature of network studies is to lie at the crossroads of different disciplines, from mathematics ( graph theory, combinatorics, probability theory) to physics ( statistical physics of networks) to computer science ( network generating algorithms, combinatorial optimization) to biological issues ( regulatory networks). New paradigms recently appeared, like that of 'scale-free networks' providing an alternative to the random graph model introduced long ago by Erdos and Renyi. With the notion of statistical ensemble and methods originally introduced for percolation networks, statistical physics is of high relevance to get a deep account of topological and statistical properties of a network. Then their consequences on the dynamics taking place in the network should be investigated. Impact of network theory is huge in all natural sciences, especially in biology with gene networks, metabolic networks, neural networks or food webs. I illustrate this brief overview with a recent work on the influence of network topology on the dynamics of coupled excitable units, and the insights it provides about network emerging features, robustness of network behaviors, and the notion of static or dynamic motif.