  Complex biological functions are carried out by the interaction of genes and
proteins. Uncovering the gene regulation network behind a function is one of
the central themes in biology. Typically, it involves extensive experiments of
genetics, biochemistry and molecular biology. In this paper, we show that much
of the inference task can be accomplished by a deep neural network (DNN), a
form of machine learning or artificial intelligence. Specifically, the DNN
learns from the dynamics of the gene expression. The learnt DNN behaves like an
accurate simulator of the system, on which one can perform in-silico
experiments to reveal the underlying gene network. We demonstrate the method
with two examples: biochemical adaptation and the gap-gene patterning in fruit
fly embryogenesis. In the first example, the DNN can successfully find the two
basic network motifs for adaptation - the negative feedback and the incoherent
feed-forward. In the second and much more complex example, the DNN can
accurately predict behaviors of essentially all the mutants. Furthermore, the
regulation network it uncovers is strikingly similar to the one inferred from
experiments. In doing so, we develop methods for deciphering the gene
regulation network hidden in the DNN "black box". Our interpretable DNN
approach should have broad applications in genotype-phenotype mapping.
