Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models
| Autor: | Zhitao Mao, Peiji Zhang, Chaoyou Xue, Xue Yang, Xin Zhao, Jingyi Cai, Ruoyu Wang, Hongwu Ma |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
chemistry.chemical_classification 0303 health sciences Chemistry Genome scale Metabolic network Bioengineering Pathway analysis Models Biological 01 natural sciences Applied Microbiology and Biotechnology 03 medical and health sciences Metabolic Model Enzyme 010608 biotechnology Escherichia coli Thermodynamics Multiple constraints Biological system Genome Bacterial Metabolic Networks and Pathways 030304 developmental biology Biotechnology |
| Zdroj: | Metabolic Engineering. 67:133-144 |
| ISSN: | 1096-7176 |
| DOI: | 10.1016/j.ymben.2021.06.005 |
| Popis: | Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect