ReviewCellular and molecular life sciences : CMLS2020
Network reduction methods for genome-scale metabolic models.
Review in Cellular and molecular life sciences : CMLS, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Water kefir as a paradigm for multi-omics and genome-scale metabolic modelling in fermented food.NPJ biofilms and microbiomes · 2026Review
- A standardized workflow for kinetic metabolic model curation and dissemination.PLoS computational biology · 2026Article
- Genome-scale metabolic modeling ofmSystems · 2025Article
- Article
- Current advances for omics-guided process optimization of microbial manufacturing.Bioresources and bioprocessing · 2023Review
- The unraveling of balanced complexes in metabolic networks.Scientific reports · 2023Article
- Functional comparison of metabolic networks across species.Nature communications · 2023Article
- FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models.Biomolecules · 2023Article
- A structural property for reduction of biochemical networks.Scientific reports · 2021Article
- The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli.BMC bioinformatics · 2021Article
- When Is an In Silico Representation a Digital Twin? A Biopharmaceutical Industry Approach to the Digital Twin Concept.Advances in biochemical engineering/biotechnology · 2021Article
- Systems biology: current status and challenges.Cellular and molecular life sciences : CMLS · 2020Review
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Authors and funding
2 authors.
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Abstract
Genome-scale metabolic models (GSMs) provide a comprehensive representation of cellular metabolism. GSMs provide a mechanistic link between cellular genotypes and metabolic phenotypes, and are thus widely used to analyze metabolism at the systems level. GSMs consist of hundreds or thousands of reactions. They have thus largely been analyzed with computationally efficient constraint-based methods such as flux-balance analysis, limiting their scope and phenotype prediction accuracy. Computationally more demanding but potentially more informative methods, such as kinetic and dynamic modeling, are currently limited to small or medium-sized models. Thus, it is desirable to achieve unbiased stoichiometric reductions of large-scale metabolic models to small, coarse-grained model representations that capture significant metabolic modules. Here, we review published automated and semiautomated methods used for large-scale metabolic model reduction. The top-down methods discussed provide minimal networks that retain a set of user-protected phenotypes, but may reduce the model's metabolic and phenotypic versatility. In contrast, the two bottom-up approaches reviewed retain a more unbiased set of phenotypes; at the same time, these methods require the partitioning of the GSM into metabolic subsystems by the user, and make strong assumptions on the subsystems' connections and their states, respectively.
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