The necessity of considering enzymes as compartments in constraint-based genome-scale metabolic models
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Abstract
As the most widespread and practical digital representations of living cells, metabolic network models have become increasingly precise and accurate. By integrating cellular resources and abiotic constraints, the prediction functions were significantly expanded in recent years. However, we found that if unreasonable modeling methods were adopted due to the lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of L-serine and L-tryptophan. Specific analysis explains the application method of EcoETM-like model, demonstrating its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Finally, we provide a preliminary comparison of the thermodynamic feasibility of ammonia and glutamine as amino donors, which revealed that the direct utilization of ammonia does not have a decisive impact on the thermodynamic feasibility of the anthranilate pathway. Our work is of great value for the structural improvement of constraints-based models.
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This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/8274030.
Reviewer: Gabriela Torres Montanaro
Supervisor: Alicia Juliana Kowaltowski
This review resulted from the graduate-level course "How to Read and Evaluate Scientific Papers and Preprints" from the University of São Paulo, which aimed to provide students with the opportunity to review scientific articles, develop critical and constructive discussions on the endless frontiers of knowledge, and understand the peer review process.
This article discusses the need to consider enzymes as compartments in genome scale metabolic models (GEMs). GEMs are computational tools that employ systems biology concepts to analyze the metabolic capabilities of organisms. An anomaly in a model was uncovered …
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/8274030.
Reviewer: Gabriela Torres Montanaro
Supervisor: Alicia Juliana Kowaltowski
This review resulted from the graduate-level course "How to Read and Evaluate Scientific Papers and Preprints" from the University of São Paulo, which aimed to provide students with the opportunity to review scientific articles, develop critical and constructive discussions on the endless frontiers of knowledge, and understand the peer review process.
This article discusses the need to consider enzymes as compartments in genome scale metabolic models (GEMs). GEMs are computational tools that employ systems biology concepts to analyze the metabolic capabilities of organisms. An anomaly in a model was uncovered that had false feasibility because of free intermediate metabolites that were assumed to be there. Through different reaction combinations, they showed that you can correct this error by incorporating enzymes as compartments. This way, they correct pathways for L-serine and L-tryptophan and proved an exchange between product yield and thermodynamic feasibility. They also did another test with the anthranilate pathway showing that the utilization of ammonia does not have a decisive impact on thermodynamic feasibility. This work developed a tool of great importance for improving GEMs and better identifying problems that go unnoticed due to lack of biological knowledge.
Major comments
Line 25: The comment on unreasonable modeling methods is accurate and very well directed; Most genome-scale metabolic models do not consider any biological data and are based only on mathematical data.
Line 93: The problem addressed by this manuscript is very well constructed.
Line 122: The concept of "bottleneck reaction" could be better explained for non-biochemical readers.
Line 397: The reason behind the balanced relation between the number of steps and MDF increase, and the maximal flux decrease, generating a yield reduction should be clearer. Why is it that the number of steps and MDF increase leading to a maximal flux decrease represents an obvious yield reduction?
Line 411: It is very nice to read this very realistic comment here; it is important to show the model can still generate some mistakes even after it is optimized.
Minor comments
Line 53: You comment that the most instructive digital method of GEM construction was developed more than 20 years ago but the citations are from 2019 and 2021. I would prefer to see the original article from 20 years ago.
Competing interests
The author declares that they have no competing interests.
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