The field of systems biology has emerged from a confluence of technological advances (DNA sequencing, gene expression profiling, proteomics, metabolomics etc.) and a “systems-level” understanding of biological processes, supported by network theory. Network models are essential in the interpretation of experimental results and, increasingly, in predicting the behaviour of cellular systems subject to experimental perturbations. One of the most challenging aspects of theoretical systems biology lies in the accurate reconstruction of biological networks. Since biological data is strongly affected by noisy measurements and incomplete and biased sampling, it can be difficult to obtain networks of sufficient accuracy to support predictive modelling. Fortunately, our understanding of the genetic mechanisms responsible for network change during evolution can provide a means to integrate the observed data from multiple species. I am interested in exploring the evolutionary history of these networks and studying the processes that have shaped their structures. In addition, the same inference methods can be used to improve network reconstructions for present-day species, contributing to better models for systems biology applications.