Predictive analytics of environmental adaptability in multi-omic network models.

Area of Science:

• Microbiology
• Computational Biology
• Systems Biology

Background:

• Bacterial adaptation to environmental changes is complex and difficult to predict.
• Adaptation involves molecular changes at multiple levels, including metabolism, gene expression, and codon usage.
• Existing models often lack the integration of these diverse molecular data.

Purpose of the Study:

• To develop a multi-omic model for predicting bacterial adaptability in Escherichia coli.
• To integrate metabolism, gene expression, and codon usage data for a comprehensive analysis.
• To create a framework for comparing bacterial responses across different conditions and strains.

Main Methods:

• Developed a multi-omic model integrating metabolism, gene expression, and codon usage.
• Implemented a multiobjective optimization algorithm to identify optimal metabolic phenotypes.
• Utilized Pareto hypervolume and spectral analysis for estimating short-term multi-omic evolution.
• Applied methods to cluster and compare experimental conditions, models, and bacterial strains.

Main Results:

• The METRADE framework successfully integrates multiple omics data for Escherichia coli.
• The multiobjective optimization algorithm identifies optimal metabolic phenotypes.
• Pareto hypervolume and spectral analysis enable comparative analysis of metabolic conditions.
• Methods were validated using a phenomics dataset of bacterial growth conditions.

Conclusions:

• METRADE provides a robust framework for assessing bacterial adaptability to environmental changes.
• The model facilitates the comparison and clustering of bacterial responses in a multidimensional objective space.
• This approach enhances the understanding of bacterial adaptation beyond traditional methods.
• The METRADE toolbox is available for broader scientific application.