  Due to the dynamic nature of biological systems, biological networks
underlying temporal process such as the development of {\it 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 reverse-engineering of the latent
sequence of temporally rewiring gene networks over more than 4000 genes during
the life cycle of \textit{Drosophila melanogaster}, given longitudinal gene
expression measurements and even when a single snapshot of such measurement
resulted from each (time-specific) 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.
