Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models

TL;DR

This paper reviews how bacterial genetics is moving beyond static genomics to integrate multiple omics layers for predictive models of bacterial function and behavior.

Contribution

The paper synthesizes recent advances in multi-omics and AI-driven approaches to build predictive models of bacterial systems.

Findings

High-resolution transcriptomics and proteomics reveal functional heterogeneity in bacterial populations.

Integration of omics data through QTL analysis links genetic variation to systems-level phenotypes.

Emerging tools like deep mutational scanning and machine learning improve bacterial genetic resolution.

Abstract

The field of bacterial systems biology is rapidly advancing beyond static genomic analyses, and moving toward dynamic, integrative approaches that connect genetic variation with cellular function. This review traces the progression from genome-wide association studies (GWAS) to multi-omics frameworks that incorporate transcriptomics, proteomics, and interactome mapping. We emphasize recent breakthroughs in high-resolution transcriptomics, including single-cell, spatial, and epitranscriptomic technologies, which uncover functional heterogeneity and regulatory complexity in bacterial populations. At the same time, innovations in proteomics, such as data-independent acquisition (DIA) and single-bacterium proteomics, provide quantitative insights into protein-level mechanisms. Experimental and AI-assisted strategies for mapping protein–protein interactions help to clarify the architecture of bacterial molecular networks. The integration of these omics layers through quantitative trait locus (QTL) analysis establishes mechanistic links between single-nucleotide polymorphisms and systems-level phenotypes. Despite persistent challenges such as bacterial clonality and genomic plasticity, emerging tools, including deep mutational scanning, microfluidics, high-throughput genome editing, and machine-learning approaches, are enhancing the resolution and scope of bacterial genetics. By synthesizing these advances, we describe a transformative trajectory toward predictive, systems-level models of bacterial life. This perspective opens new opportunities in antimicrobial discovery, microbial engineering, and ecological research.

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