Predicting microbial community dynamics with integrated meta-omics

Researchers have developed a framework for forecasting the dynamics of complex microbial communities using integrated meta-omics data. The study focused on a microbial community in a biological wastewater treatment plant (BWWTP) and used a combination of techniques including singular value decomposition (SVD) and autoregressive integrated moving average (ARIMA) modeling. The researchers identified 17 temporal signals representing different ecological events in the community and established causal relationships between these signals. The framework successfully predicted gene abundance and expression in the microbial community, demonstrating its potential for understanding and managing microbial ecosystems.