Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology

Area of Science:

• Microbiology
• Systems Biology
• Computational Biology

Background:

• Integrated multi-omics approaches are increasingly adopted in microbiology.
• These methods enable detection of hidden microbial cell features, such as multi-scale spatial organization.
• They also allow tracing molecular components across various cellular functional states.

Purpose of the Study:

• To present the opportunities and challenges of multi-omics data integration in systems biology.
• To discuss the importance of biological information layers for biotechnological and clinical applications.
• To focus on bacterial metabolism and modeling procedures within multi-omics data integration.

Main Methods:

• Review of recent computational tools for large-scale dataset integration.
• Discussion of multi-omics data integration strategies in current systems biology pipelines.
• Framework proposal for designing systems biology experiments.

Main Results:

• Multi-omics integration reduces the knowledge gap between genotype and phenotype.
• It poses new challenges and opportunities for computational microbiologists.
• Identification of key biological information layers crucial for biotechnological and clinical purposes.

Conclusions:

• The integration of diverse omics data is essential for understanding microbial complexity.
• Effective multi-omics data integration requires advanced computational tools and strategic experimental design.
• This approach is vital for advancing microbiology, systems biology, and related applications.