Representing and analysing complex networks remains a roadblock to creating dynamic network models of biological processes and pathways. The study of cell fate transitions can reveal much about the transcriptional regulatory programs that underlie these phenotypic changes and give rise to the coordinated patterns in expression changes that we observe. The application of gene expression state-space trajectories to capture cell fate transitions at the genomewide level is one approach currently used in the literature. The observed convergence of these trajectories has often been attributed to the existence of attractive sites (attractors) in the expression landscape, as popularised by the work of Stuart Kauffman. In this talk, I will demonstrate how we propose an alternative interpretation that explains this convergent behaviour by recognising that there are two types of processes participating in these cell fate transitions— core processes transient processes that capture those pathways, and responses specific to the inducer.