Metabolic Flux Analysis in Isotope Labeling Experiments using the Adjoint Approach
This work provides a faster method for MFA, benefiting researchers studying metabolic pathways through isotope labeling experiments.
The paper addresses the computational bottleneck of derivative computation in metabolic flux analysis (MFA) for isotope labeling experiments. Using the adjoint approach reduces computation time significantly compared to the direct approach, validated on E. coli metabolic pathways.
Comprehension of metabolic pathways is considerably enhanced by metabolic flux analysis (MFA-ILE) in isotope labeling experiments. The balance equations are given by hundreds of algebraic (stationary MFA) or ordinary differential equations (nonstationary MFA), and reducing the number of operations is therefore a crucial part of reducing the computation cost. The main bottleneck for deterministic algorithms is the computation of derivatives, particularly for nonstationary MFA. In this article we explain how the overall identification process may be speeded up by using the adjoint approach to compute the gradient of the residual sum of squares. The proposed approach shows significant improvements in terms of complexity and computation time when it is compared with the usual (direct) approach. Numerical results are obtained for the central metabolic pathways of Escherichia coli and are validated against reference software in the stationary case. The methods and algorithms described in this paper are included in the sysmetab software package distributed under an Open Source license at http://forge.scilab.org/index.php/p/sysmetab/.