Functional Microbial Landscapes: Integrating Multi-Omics, Bioinformatics, and Environmental Insights

Planet Earth's biosphere is predominantly shaped by microorganisms, which govern biogeochemical processes and play critical roles in maintaining ecological and evolutionary balance. With the advent of next-generation sequencing (NGS) and systems biology, the study of biological molecules and microbial systems—encompassed under the "omics" umbrella (genomics, transcriptomics, proteomics, metabolomics, microbiomics)—has significantly advanced our understanding of microbial dynamics. However, to fully decipher the functional relevance of microbial communities, there is a growing need to integrate omics data with ecological, evolutionary, and mathematical models.
This Research Topic aims to unite microbiologists, ecologists, computational biologists, and bioinformaticians under a broader scientific question: How can integrative omics and predictive modeling reshape our understanding of microbial ecosystems and their roles in health, agriculture, biotechnology, and climate resilience? Beyond methods alone, we seek to explore the conceptual frameworks that link microbial function to system-level behaviour through network theory, machine learning, statistical ecology, and systems dynamics.

We welcome Original Research and Review Articles that address themes such as:

• Integrative multi-omics analyses revealing microbial roles in ecosystem processes.
• Functional microbial ecology: from genes to biogeochemical cycles.
• Theoretical and mathematical models for predicting microbiome behavior and community resilience.
• Linking microbial diversity and metabolic potential to environmental and host-associated outcomes.
• Data-driven discovery of keystone taxa and microbial interactions using statistical and computational approaches.
• Advances in microbial trait-based modeling, CRISPR/Cas systems, and evolutionary dynamics.
• Novel insights into microbial roles in antimicrobial resistance, plant-microbe interactions, and synthetic biology.
• Machine learning and artificial intelligence tools for forecasting microbial shifts under changing environmental conditions.
• Comparative studies of microbiomes across different biomes and hosts using standardized ecological indices.
• Viral metagenomics and phage-host dynamics in microbial community structuring.

Please note that Systems Microbiology does not consider descriptive studies that are solely based on amplicon (e.g., 16S rRNA, 18S rRNA, ITS and other marker genes) profiles, unless they are accompanied by a clear hypothesis and experimentation, and provide insight into the microbiological system or process being studied.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Microbial Ecology, Multi-Omics Integration, Systems Biology, Machine Learning in Microbiology, Microbiome Dynamics, Environmental Microbiology, Mathematical Biology, Predictive Modeling

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.