The effective structure of complex networks: Canalization in the dynamics of complex networks drives dynamics, criticality and control


Network Science has provided predictive models of many complex systems from molecular biology to social interactions. Most of this success is achieved by reducing multivariate dynamics to a graph of static interactions. Such network structure approach has provided many insights about the organization of complex systems. However, there is also a need to understand how to control them; for example, to revert a diseased cell to a healthy state in systems biology models of biochemical regulation. Based on recent work [1, 2] we show that the control of complex networks crucially depends on redundancy that exists at the level of variable dynamics. To understand the effect of such redundancy, we study automata networks− both systems biology models and large random ensembles of Boolean networks (BN). In these discrete dynamical systems, redundancy is conceptualized as canalization: when a subset of inputs is sufficient to determine the output of an automaton. We discuss two types of canalization: effective connectivity and input symmetry [2].

The 5th International Workshop on Complex Networks & Their Applications