multiTFA: a Python']Python package for multi-variate thermodynamics-based flux analysis

被引:7
|
作者
Mahamkali, Vishnuvardhan [1 ]
McCubbin, Tim [1 ]
Beber, Moritz Emanuel [2 ]
Noor, Elad [3 ]
Marcellin, Esteban [1 ]
Nielsen, Lars Keld [1 ,2 ]
机构
[1] Univ Queensland, Australian Inst Bioengn & Nanotechnol AIBN, Brisbane, Qld 4072, Australia
[2] Tech Univ Denmark, Novo Nordisk Fdn, Ctr Biosustainabil, DK-2800 Lyngby, Denmark
[3] Weizmann Inst Sci, Dept Plant & Environm Sci, IL-7610001 Rehovot, Israel
基金
澳大利亚研究理事会;
关键词
MODELS;
D O I
10.1093/bioinformatics/btab151
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: We achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables. Results: We present multiTFA, a Python implementation of our framework. We evaluated our application using the core Escherichia coli model and achieved a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges, while three out of 12 reactions in glycolysis changed from reversible to irreversible.
引用
收藏
页码:3064 / 3066
页数:3
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