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中国生物工程杂志

China Biotechnology
China Biotechnology  2022, Vol. 42 Issue (1/2): 128-138    DOI: 10.13523/j.cb.2108068
Orginal Article     
Research Progress and Application of Metabolic Network Model Constrained by Thermodynamic Principles
YU Si-qian1,XIA Jian-ye1,**(),ZHUANG Ying-ping1,2
1 State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
2 Qingdao Innovation Research Institute, East China University of Science and Technology, Qingdao 266102, China
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Abstract  

High-throughput omics technology provided detailed data for studying life system components. Through the interaction of components among the genome, transcriptome, proteome, and metabolome, it promoted the construction of the genome scale of metabolic network model (GSMM). GSMM, as a commonly used tool in systems biology, allows the complex life process of cells to be studied as a whole system, so it shows more holistic thinking contrary to traditional reductionism. Flux balance analysis (FBA), as the mainstream method of GSMM, is usually difficult to obtain the unique optimal solution due to enough constraints. Thermodynamics is closely related to biological metabolism, so in addition to introducing multiple omics data into the GSMM as additional constraints, adding thermodynamic constraints has also become an effective way to reduce the solution space further. This paper first reviews the method of introducing thermodynamic constraints into the GSMM and the advantages and disadvantages of the methods themselves, and then summarizes the methods and tools for obtaining relevant thermodynamic parameters. Finally, this review introduces the metabolic network model integrating multi-omics and thermodynamic constraints and discusses the practical application of the model based on thermodynamic principle constraints, and puts forward a prospect on how to apply thermodynamic constraints to improve the accuracy of the GSMM.



Key wordsGenome-scale metabolic network model(GSMM)      Thermodynamics      Constraint analysis      Gibbs free-energy     
Received: 29 August 2021      Published: 03 March 2022
ZTFLH:  Q819  
Corresponding Authors: Jian-ye XIA     E-mail: jyxia@ecust.edu.cn
Cite this article:

YU Si-qian,XIA Jian-ye,ZHUANG Ying-ping. Research Progress and Application of Metabolic Network Model Constrained by Thermodynamic Principles. China Biotechnology, 2022, 42(1/2): 128-138.

URL:

https://manu60.magtech.com.cn/biotech/10.13523/j.cb.2108068     OR     https://manu60.magtech.com.cn/biotech/Y2022/V42/I1/2/128

方法 相同点 不同点 实现方法的工具 求解类型
NET 1.需要热力学参数△rG0和△fG0的输入;
2.需要代谢组数据的输入;
3.参数通过热力学第二定律进行耦合:△rG=△r Go+RT ln Q;
4.目的都是为了去除热力学上不可行的反应或途径;
5.均适于基因组规模的分析
1.需要预先确定通量的方向;
2.可对输入的代谢组学数据进行热力学一致性质量检验
Matlab:anNET 非线性规划
(NLP)
TFA 1.需对可逆反应进行拆分,得到正、反两个反应;
2.引入了离散变量zi对反应方向进行分配
Matlab:matTFA
Python:pyTFA
混合整数线性规划
(MILP)
Table 1 Comparison of constraint analysis methods NET and TFA based on thermodynamic parameters
Fig.1 Databases and tools for obtaining thermodynamic parameters (a) Methods of estimating thermodynamic parameters (b) Database and tools for obtaining thermodynamic parameters
Fig.2 The development of thermodynamic parameters and analytical methods Purple represents the method of estimating △G0, yellow represents the analysis method based on thermodynamic principles, and green represents the comprehensive analysis method with integrated thermodynamic constraints
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