Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models

Current genome-wide metabolic models rely on assumptions about flux boundaries. These assumptions can strongly influence predictions. Prior research has shown that poor boundary choices may introduce errors in flux balance analysis (FBA) outcomes. While FBA is widely used, its accuracy depends heavily on parameter selection. The role of essential amino acid intake in metabolic models is already known. However, no prior work had resolved how to systematically identify key fluxes. This gap motivated the development of a sensitivity-based approach. A rational modeling strategy requires identifying pivotal parameters. No existing method efficiently handles genome-wide sensitivity analysis at scale.

This study aims to develop a method for identifying sensitive flux parameters in genome-wide metabolic models. The goal is to reduce errors caused by arbitrary flux boundary choices. The focus is on automating the identification of key fluxes using sensitivity analysis. The method seeks to handle the computational burden of genome-wide models. It addresses the challenge of evaluating many FBA simulations efficiently. The study proposes using variance-based analysis to detect sensitive parameters. It introduces a master-slave computing approach to distribute calculations. The ultimate purpose is to support accurate model calibration through global sensitivity analysis.

The method uses variance-based sensitivity analysis to detect key fluxes. It applies Saltelli’s method to generate parameter variations. Flux Balance Analysis is used to simulate model behavior under different conditions. The approach leverages master-slave architecture for parallel computing. It distributes FBA simulations across multi-core systems using MPI. The method collects and analyzes results from thousands of model runs. It focuses on identifying parameters with high sensitivity coefficients. The workflow includes parameterization, simulation, and global sensitivity assessment.

The study identified sensitive parameters in Recon2.2 and Recon3D models. In Recon2.2, sensitive parameters were linked to essential amino acid intake. In Recon3D, sensitive parameters were associated with phospholipid metabolism. The analysis revealed significant higher-order interaction effects. These interactions suggest that parameter combinations influence model outcomes. The method successfully scaled to genome-wide models using distributed computing. Results showed that small parameter changes can cause large outcome variations. The study demonstrated the feasibility of global sensitivity analysis on large models.

The study supports the importance of global sensitivity analysis in metabolic modeling. It shows that key fluxes can be identified automatically and efficiently. The findings suggest that parameter interactions should be considered during model calibration. The method provides a scalable solution for genome-wide sensitivity analysis. It uses parallel computing to handle the computational load of FBA simulations. The results indicate that essential amino acid and phospholipid fluxes are sensitive in Recon models. The approach helps reduce errors from arbitrary boundary assumptions. The study proposes that global strategies should be adopted in model development.