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. The 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 body of knowledge. The DNN model could lay a foundation of "in-silico-embryo", which can regenerate a great variety of interesting phenomena, and 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 systems .
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 .
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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
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
R
. 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. The 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 body of knowledge. The
, a form of machine learning or artificial intelligence. Specifically, the
R
model could lay a foundation of "in-silico-embryo", which can regenerate a great variety of interesting phenomena, and
learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system,
R
all kinds of perturbations to discover underlying mechanisms. This approach can be readily applied to a variety of complex biological systems
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