Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models
نویسندگان
چکیده
Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict phenotypes. In addition stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM considering either enzymatic or shown improve prediction accuracy. this paper, we propose a novel method that integrates both in single Pyomo modeling framework (ETGEMs). We applied construct EcoETM ( E. coli model with constraints). Using model, calculated optimal pathways for cellular growth production of 22 metabolites. When comparing results those i ML1515 one two constraints, observed many thermodynamically unfavorable and/or high cost were excluded from EcoETM. For example, synthesis pathway carbamoyl-phosphate (Cbp) is enzymatically costly. After introducing new yields several Cbp-derived products (e.g. L -arginine, orotate) using more realistic. The study demonstrate great application potential multiple analysis prediction. • constraints. multi-constraints formulation open-source code. Various variability identify bottleneck reactions key enzymes. More reliable due favorable arrangement reactions.
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ژورنال
عنوان ژورنال: Metabolic Engineering
سال: 2021
ISSN: ['1096-7176', '1096-7184']
DOI: https://doi.org/10.1016/j.ymben.2021.06.005