Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model

Area of Science:

• Systems Biology
• Metabolic Engineering

Background:

• Modern sequencing has increased reconstructed metabolic networks, enabling analysis of high-throughput omics data.
• Omics data (transcriptomics, proteomics, metabolomics) provide insights into metabolic activity and regulation.
• Metabolomics specifically reveals enzyme activity via metabolic regulation and mass action.

Purpose of the Study:

• Introduce integrative omics-metabolic analysis (IOMA) for quantitative integration of proteomic and metabolomic data with metabolic models.
• Improve prediction accuracy of metabolic flux distributions.

Main Methods:

• Formulate IOMA as a quadratic programming (QP) problem.
• Seek steady-state flux distribution consistent with kinetically derived estimations and measured omics data.

Main Results:

• IOMA accurately predicts human erythrocyte metabolic states, outperforming flux balance analysis and minimization of metabolic adjustment.
• IOMA correctly predicts Escherichia coli metabolic fluxes under gene knockouts with higher accuracy than existing methods.

Conclusions:

• IOMA offers a significant advantage for predicting metabolic flux distributions using integrated omics data.
• The method is expected to greatly contribute to future cellular metabolism research, given the increasing availability of proteomic and metabolomic data.