Spatiotemporal network motif reveals the biological traits of developmental gene regulatory networks in Drosophila melanogaster

On the basis of this concept, we analyzed the developmental gene regulatory network of the Drosophila melanogaster embryo. We identified spatiotemporal network motifs and investigated their distribution pattern in time and space. As a result, we found how key developmental processes are temporally and spatially regulated by the gene network. In particular, we found that nested feedback loops appeared frequently throughout the entire developmental process. From mathematical simulations, we found that mutual inhibition in the nested feedback loops contributes to the formation of spatial expression patterns.Taken together, the proposed concept and the simulations can be used to unravel the design principle of developmental gene regulatory networks.To uncover the governing principles underlying complex biological processes, it is important to understand the relationship between topological structures and the dynamical characteristics of gene regulatory networks [1-4]. One promising method of investigation is to disassemble the large regulatory network into its more basic, constituent building blocks called network motifs, which recur within a network much more often than expected in random networks. Network motifs are considered to have been evolutionarily selected because of their functional advantages [5].Most previous studies have identified network motifs of biological networks by implicitly assuming that all the links in a network can be active or working at the same time. However, such approaches may not be applicable to developmental networks where all genes and interactions do not operate simultaneously due to spatial and temporal variations. Some network motif approaches have partially considered spatial or temporal information on biological networks [6,7]. Papatsenko analyzed the dynamics of network motifs for a spatial stripe pattern formation, only in early embryogenesis [7], while Kim et al. explored the dynamics for temporal network motifs [6]. Nevertheles