Metabolic Flux Analysis in Isotope Labeling Experiments using the Adjoint Approach

TL;DR

This paper introduces an adjoint approach to accelerate metabolic flux analysis in isotope labeling experiments, significantly reducing computation time and complexity, validated through numerical results on E. coli pathways.

Contribution

It presents a novel adjoint-based method for faster gradient computation in MFA-ILE, improving efficiency over traditional direct approaches.

Findings

Significant reduction in computation time for nonstationary MFA.

Validated accuracy of the adjoint approach against reference software.

Implementation included in open-source sysmetab package.

Abstract

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/.