Principles of proteome allocation are revealed using proteomic data and genome-scale models.

Area of Science:

• Systems biology
• Computational biology
• Metabolic engineering

Background:

• Constraint-based modeling is crucial for understanding cellular processes.
• Integrating omics data with genome-scale models enhances predictive accuracy.
• Metabolism and Macromolecular Expression (ME) models link proteome allocation to cellular functions.

Purpose of the Study:

• To develop a context-specific ME model of Escherichia coli by incorporating proteomics data.
• To improve the prediction of bacterial proteome and phenotype across diverse growth conditions.
• To establish a flexible formalism for integrating omics data into constraint-based models.

Main Methods:

• Formulated proteome allocation constraints using proteomics data for key sectors within an ME model.
• Calibrated the ME model using wild-type Escherichia coli data across 15 different growth environments.
• Validated model predictions against experimental data for growth rate and metabolic fluxes.

Main Results:

• The calibrated ME model accurately predicted the generalist E. coli proteome and phenotype.
• Prediction errors for growth rate and metabolic fluxes were reduced by 69% and 14%, respectively.
• Identified enrichment of general stress response sigma factor (σS) in constrained proteome sectors, indicating a "hedging" strategy.

Conclusions:

• Sector-constrained ME models provide a robust framework for integrating omics data into systems-level biological models.
• This approach enhances the predictive power of constraint-based models for microbial systems.
• The developed formalism offers an accessible method to bridge the gap between omics data complexity and model-based predictions.