On the Robustness of Elementary-Flux-Modes-based Metabolic Flux Analysis
(English)Manuscript (preprint) (Other academic)
Elementary flux modes (EFMs) are vectors defined from a metabolic reaction network, giving the connections between substrates and products. EFMs-based metabolic flux analysis (MFA) estimates the flux over each EFM from external flux measurements through least-squares data fitting. The measurements used in the data fitting are subject to errors. A robust optimization problem includes information on errors and gives a way to examine the sensitivity of the solution of the EFMs-based MFA to these errors. In general, formulating a robust optimization problem may make the problem significantly harder. We show that in the case of the EFMs-based MFA the robust problem can be stated as a convex quadratic programming problem. We have previously shown how the data fitting problem may be solved in a column-generation framework. In this paper, we show how column generation may be applied also to the robust problem. Furthermore, the option to indicate intervals on metabolites that are not measured is introduced in this column generation framework. The robustness of the data is evaluated in a case-study, which indicated that the solutions of our non-robust problems are in fact near-optimal also when robustness is considered, implying that the errors in measurement do not have a large impact on the optimal solution. Furthermore, we showed that the addition of intervals on unmeasured metabolites resulted in a change in the optimal solution.
Metabolic Network; Robust Optimization; Least-squares; Elementary Flux Mode; Chinese Hamster Ovary Cell
Computational Mathematics Bioinformatics and Systems Biology
IdentifiersURN: urn:nbn:se:kth:diva-172372OAI: oai:DiVA.org:kth-172372DiVA: diva2:847415
FunderSwedish Research CouncilVINNOVA
QS 20152015-08-202015-08-202015-08-27Bibliographically approved