Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

Computational models can help predict metabolic profiles of microbial communities such as human gut microbiomes or environmental microbiomes, but they lack generalizability and interpretability. To address this challenge, Wang et al. report a deep learning approach for metabolic profile prediction called mNODE that incorporates a neural network module with hidden layers described by ordinary differential equations. Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, whereas sequencing methods quantifying the species composition of microbial communities are well developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability and great interpretability. Here we develop a method called metabolomic profile predictor using neural ordinary differential equations (mNODE), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Furthermore, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

Automated summary

The authors introduce a deep learning method called mNODE for predicting metabolic profiles of microbial communities. mNODE outperforms existing methods and can reveal microbe-metabolite interactions, providing valuable insights for precision nutrition research.