Background: Genome-wide reconstructions of metabolism opened the way to thorough investigations of cell metabolism for health care and industrial purposes. However, the predictions offered by Flux Balance Analysis (FBA) can be strongly affected by the choice of flux boundaries, with particular regard to the flux of reactions that sink nutrients into the system. To mitigate possible errors introduced by a poor selection of such boundaries, a rational approach suggests to focus the modeling efforts on the pivotal ones. Methods: In this work, we present a methodology for the automatic identification of the key fluxes in genome-wide constraint-based models, by means of variance-based sensitivity analysis. The goal is to identify the parameters for which a small perturbation entails a large variation of the model outcomes, also referred to as sensitive parameters. Due to the high number of FBA simulations that are necessary to assess sensitivity coefficients on genome-wide models, our method exploits a master-slave methodology that distributes the computation on massively multi-core architectures. We performed the following steps: (1) we determined the putative parameterizations of the genome-wide metabolic constraint-based model, using Saltelli’s method; (2) we applied FBA to each parameterized model, distributing the massive amount of calculations over multiple nodes by means of MPI; (3) we then recollected and exploited the results of all FBA runs to assess a global sensitivity analysis. Results: We show a proof-of-concept of our approach on latest genome-wide reconstructions of human metabolism Recon2.2 and Recon3D. We report that most sensitive parameters are mainly associated with the intake of essential amino acids in Recon2.2, whereas in Recon 3D they are associated largely with phospholipids. We also illustrate that in most cases there is a significant contribution of higher order effects. Conclusion: Our results indicate that interaction effects between different model parameters exist, which should be taken into account especially at the stage of calibration of genome-wide models, supporting the importance of a global strategy of sensitivity analysis.
Bibliographical noteFunding Information:
The institutional financial support to SYSBIO.ISBE.IT within the Italian Roadmap for ESFRI Research Infrastructures and the FLAG-ERA grant ITFoC are gratefully acknowledged. Financial support from the Italian Ministry of University and Research (MIUR) through grant ‘Dipartimenti di Eccellenza 2017’ to University of Milano Bicocca, Department of Biotechnology and Biosciences is also greatly acknowledged.
© 2021, The Author(s).
Copyright 2021 Elsevier B.V., All rights reserved.
- Flux Balance Analysis
- Genome-wide models
- Global sensitivity analysis
- High-performance computing
- Sobol coefficients
- Metabolic Networks and Pathways
- Models, Biological
- Metabolic Flux Analysis