ArticlePLoS computational biology2017
redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
- Systems biology during 20 years of PLoS Computational Biology.PLoS computational biology · 2026Article
- Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration.Nature communications · 2026Article
- Systematic data-driven genome-scale metabolic model reduction for bioprocess modeling: CHO culture case study.NPJ systems biology and applications · 2026Article
- In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.Microbiome · 2026Article
- Rational strain design with minimal phenotype perturbation.Nature communications · 2024Article
- Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states.Nature catalysis · 2024Article
- From microbiome composition to functional engineering, one step at a time.Microbiology and molecular biology reviews : MMBR · 2023Review
- Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer.iScience · 2023Article
- Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model.PLoS computational biology · 2023Article
- Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding.PLoS computational biology · 2023Article
- Metabolic Modeling ofMetabolites · 2023Article
- Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer.bioRxiv : the preprint server for biology · 2023Article
- The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility.PLoS computational biology · 2022Article
- Protein cost minimization promotes the emergence of coenzyme redundancy.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- A structural property for reduction of biochemical networks.Scientific reports · 2021Article
- TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model ofBioengineering (Basel, Switzerland) · 2021Article
- Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.PLoS computational biology · 2021Article
- Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis.Scientific reports · 2021Article
- The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli.BMC bioinformatics · 2021Article
- Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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4 authors.
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Abstract
Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these "consistently-reduced" models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.
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