Use of 'omics' databases to elucidate long-time assumptions in microbiology.
• Scaling modeling of microbial interactions to the diversity found in natural ecosystems: Definition of concepts and theories to use partial genome-scale metabolic modeling as a tool to explore microbial community interactions.
• Species in the era of metagenome assembled genomes: We are developing techniques to exploit the diversity of metagenome assembled genomes by resolving species (and potentially strain) variation in complex microbial communities.
• Exploration of regulatory network in complex microbial communities: We are developing deep learning approaches to map transcription factors (and potentially their binding sites) in complex microbial communities.
• High-throughput genomic analysis: with the exponential increase of genomic (and multi-omics) data available in public repositories, we are creating user-friendly pipelines that can be used by microbiologists and microbial ecologists to analyze hundreds or thousands of genomes simultaneously.