期刊
BIOINFORMATICS
卷 37, 期 18, 页码 3064-3066出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab151
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资金
- Novo Nordisk Foundation under NFF [NNF10CC1016517, NNF14OC0009473]
- Australian Research Council Centre of Excellence in Synthetic Biology [CE200100029]
The study introduced multivariate treatment and component contribution method to improve thermodynamic-based flux analysis (TFA), greatly reducing the uncertainty of thermodynamic variables. Using the Escherichia coli model, a significant reduction in Gibbs free energy ranges and some reactions changing from reversible to irreversible were achieved.
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.
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