Before revision: 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 overfitting or underfitting and thus inadequate predictive power, especially when dealing with systems of high complexity. This problem could be overcome by deep neuron network (DNN) . Choosing pattern formation of the gap genes in Drosophila early embryogenesis as an example, we established a differential equation model whose synthesis term is expressed as a DNN.
After revision: 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) . Choosing pattern formation of the gap genes in Drosophila early embryogenesis as an example, we established a differential equation model whose synthesis term is expressed as a DNN.