Dynamics of complex biological systems is driven by intricate networks,the current knowledge of which are often incomplete.The traditional systems biology modeling usually implements an ad hoc fixed set of differential equations with predefined function forms.Such an approach often suffers from over-fitting the data and inadequate predictive power,especially when dealing with systems of high complexity.This problem could be overcome by deep neuron network.Choosing pattern formation of the gap genes in Drosophila early embryogenesis as an example,we established a deep neural network(DNN).The trained DNN model yields perfect fitting and impressively accurate predictions on mutant patterns.We further mapped the trained DNN into a simplified conventional regulation network,which is consistent with the existing knowledge.The DNN model could lay a foundation of "in-silico-embryo" on which one can perform all kinds of perturbations to discover underlying mechanisms.This approach can be readily applied to a variety of complex biological networks.