Literature DB >> 30004883

A Promising Method for Calculating True Steady-State Metabolite Concentrations in Large-Scale Metabolic Reaction Network Models.

Atsuko Miyawaki-Kuwakado, Soichiro Komori, Fumihide Shiraishi.   

Abstract

The calculation of steady-state metabolite concentrations in metabolic reaction network models is the first step in the sensitivity analysis of a metabolic reaction system described by differential equations. However, this calculation becomes very difficult when the number of differential equations is more than 100. In the present study, therefore, we investigated a calculation procedure for obtaining true steady-state metabolite concentrations both efficiently and accurately even in large-scale network models. For convenience, a linear pathway model composed of a simple Michaelis-Menten rate law and two TCA cycle models were used as case studies. The calculation procedure is as follows: first solve the differential equations by a numerical method for solving initial-value problems until the upper several digits of the calculated values stabilize, and then use these values as initial guesses for a root-finding technique. An intensive investigation indicates that the S-system technique, finding roots in logarithmic space and providing a broader convergence region, is superior to the Newton-Raphson technique, and the algorithm using the S-system technique successfully provides true steady-state values with machine accuracy even with 1,500 differential equations. The complex-step method is also shown to contribute to shortening the calculation time and enhancing the accuracy. The program code has been deposited to https://github.com/BioprocessdesignLab/Steadystateconc.

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Year:  2018        PMID: 30004883     DOI: 10.1109/TCBB.2018.2853724

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  2 in total

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Authors:  Wenzheng Bao; Xiao Lin; Bin Yang; Baitong Chen
Journal:  Front Genet       Date:  2022-05-19       Impact factor: 4.772

2.  An Internet-Oriented Multilayer Network Model Characterization and Robustness Analysis Method.

Authors:  Yongheng Zhang; Yuliang Lu; Guozheng Yang; Dongdong Hou; Zhihao Luo
Journal:  Entropy (Basel)       Date:  2022-08-18       Impact factor: 2.738

  2 in total

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