From Genotypes to Ecosystems: Unraveling Microbial Interactions through Machine Learning to Engineer Stable Synthetic Communities

Microbial communities are intricate ecosystems of interacting microorganisms in a shared environment. Discerning each dimension of these interactions is crucial to unraveling the principles governing the range of community behavior and their dynamics. However, the complexity and scale of microbial communities lead to a combinatorial explosion of potential community interactions and pose significant challenges for computational modeling and experimental exploration.

In this project, we propose a machine-learning framework that predicts interactions in a microbial community using a suite of novel features derived from the member genotypes. The microbial interaction classifier is being designed to accurately predict the type and strength of interactions between different community members in a given environment. It uses novel scoring metrics incorporating metabolic cooperation and competition, secondary metabolite inhibition, and functional similarity scores extracted from the genomes and reconstructed metabolic models as features. These metrics are designed to capture the different ways in which microbes can interact with each other. The classifier is being trained on a large dataset of microbial interactions, and it is expected to accurately predict interactions between diverse microbes in new environments. Additionally, we aim to utilize this classifier to screen microbial pairings for stable combinations that can constitute a rationally designed synthetic community. This approach can help circumvent experimental limitations and enable controlled experimentation and systematic exploration of microbial community dynamics.

Our machine learning-based approach will provide a valuable framework for predicting microbial interactions, reducing reliance on extensive experimental efforts and accelerating progress in synthetic community design for various applications.