Multiplex methods provide effective integration of multi-omic data in genome-scale models.

Area of Science:

• Microbial Ecology
• Systems Biology
• Genomics

Background:

• Bacterial adaptation to environmental changes is driven by genomic, transcriptomic, and metabolic variations.
• Understanding genotype-phenotype relationships is key for predicting these effects.
• Current methods lack the ability to characterize interactions between multiple environmental factors.

Purpose of the Study:

• To develop a method for extracting network-level information from collections of environmental conditions.
• To integrate multiple omic levels (transcriptomic, fluxomic) to analyze bacterial responses.
• To infer similarities between growth conditions using a multi-omic network approach.

Main Methods:

• Modeled a compendium of growth conditions as a multiplex network with transcriptomic and fluxomic layers.
• Proposed a multi-omic network approach to infer condition similarity by integrating network layers.
• Fused network layers into a single network to capture global condition similarities across omic levels.
• Applied the multi-omic fusion to a genome-scale reconstruction of Escherichia coli.

Main Results:

• Developed a method to infer condition similarity by integrating transcriptomic and fluxomic data.
• Created a global network of conditions and their similarities across two omic levels.
• Successfully applied the multi-omic fusion to an Escherichia coli model.

Conclusions:

• The method enables evaluation and cross-comparison of condition collections across different species.
• Multi-omic data on experimental condition topology allows inference of untested or incomplete profiles.
• The weighted network fusion method for genome-scale models is publicly available.