利用共轭方法进行同位素标记实验中的代谢流分析
Metabolic Flux Analysis in Isotope Labeling Experiments using the Adjoint Approach
AI总结：
本文提出利用共轭方法加速代谢流分析中的梯度计算，提升计算效率，并通过大肠杆菌中心代谢途径的数值结果验证了方法的有效性。
摘要：
代谢流分析（MFA-ILE）在同位素标记实验中显著增强了对代谢途径的理解。平衡方程由数百个代数方程（静态MFA）或常微分方程（非静态MFA）组成，因此减少运算次数是降低计算成本的关键部分。确定性算法的主要瓶颈在于导数的计算，特别是非静态MFA。本文解释了如何通过共轭方法计算残差平方和的梯度来加速整体识别过程。所提出的方法在复杂性和计算时间方面相比传统（直接）方法有显著改进。通过大肠杆菌的中心代谢途径获得数值结果，并在静态情况下的参考软件中进行了验证。本文描述的方法和算法已包含在sysmetab软件包中，该软件包以开源许可证分发，在http://forge.scilab.org/index.php/p/sysmetab/上可用。
英文摘要：
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/.
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