Due to the dynamic nature of biological systems, biological networks underlying temporal process such as the development of Drosophila melanogaster can exhibit significant topological changes to facilitate dynamic regulatory functions. Thus it is essential to develop methodologies that capture the temporal evolution of networks, which make it possible to study the driving forces underlying dynamic rewiring of gene regulation circuity, and to predict future network structures. Using a new machine learning method called Tesla, which builds on a novel temporal logistic regression technique, we report the first successful genome-wide reverseengineering of the latent sequence of temporally rewiring gene networks over more than 4000 genes during the life cycle of Drosophila melanogaster, given longitudinal gene expression measurements and even when a single snapshot of such measurement resulted from each (timespecific) network is available. Our methods offer the first glimpse of time-specific snapshots and temporal evolution patterns of gene networks in a living organism during its full developmental course. The recovered networks with this unprecedented resolution chart the onset and duration of many gene interactions which are missed by typical static network analysis, and are suggestive of a wide array of other temporal behaviors of the gene network over time not noticed before.

28 pages

*Equally contributing authors
**Corresponding author

This page maintained by [email protected]