ArticlePLoS computational biology2016
Improved Metabolic Models for E. coli and Mycoplasma genitalium from GlobalFit, an Algorithm That Simultaneously Matches Growth and Non-Growth Data Sets.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Article
- A dual-scale fused hypergraph convolution-based hyperedge prediction model for predicting missing reactions in genome-scale metabolic networks.Briefings in bioinformatics · 2024Article
- Path to improving the life cycle and quality of genome-scale models of metabolism.Cell systems · 2021Review
- Mechanistic models of microbial community metabolism.Molecular omics · 2021Review
- PhenoMapping: a protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models.STAR protocols · 2021Article
- The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes.Nucleic acids research · 2021Article
- Article
- Systems biology analysis of the Clostridioides difficile core-genome contextualizes microenvironmental evolutionary pressures leading to genotypic and phenotypic divergence.NPJ systems biology and applications · 2020Article
- Article
- Flux balance analysis with or without molecular crowding fails to predict two thirds of experimentally observed epistasis in yeast.Scientific reports · 2019Article
- A systematic assessment of current genome-scale metabolic reconstruction tools.Genome biology · 2019Article
- Flux tope analysis: studying the coordination of reaction directions in metabolic networks.Bioinformatics (Oxford, England) · 2019Article
- Each of 3,323 metabolic innovations in the evolution ofProceedings of the National Academy of Sciences of the United States of America · 2019Article
- Erroneous energy-generating cycles in published genome scale metabolic networks: Identification and removal.PLoS computational biology · 2017Article
- Discovering missing reactions of metabolic networks by using gene co-expression data.Scientific reports · 2017Article
- Correction: Improved Metabolic Models for E. coli and Mycoplasma genitalium from GlobalFit, an Algorithm That Simultaneously Matches Growth and Non-Growth Data Sets.PLoS computational biology · 2016Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Constraint-based metabolic modeling methods such as Flux Balance Analysis (FBA) are routinely used to predict the effects of genetic changes and to design strains with desired metabolic properties. The major bottleneck in modeling genome-scale metabolic systems is the establishment and manual curation of reliable stoichiometric models. Initial reconstructions are typically refined through comparisons to experimental growth data from gene knockouts or nutrient environments. Existing methods iteratively correct one erroneous model prediction at a time, resulting in accumulating network changes that are often not globally optimal. We present GlobalFit, a bi-level optimization method that finds a globally optimal network, by identifying the minimal set of network changes needed to correctly predict all experimentally observed growth and non-growth cases simultaneously. When applied to the genome-scale metabolic model of Mycoplasma genitalium, GlobalFit decreases unexplained gene knockout phenotypes by 79%, increasing accuracy from 87.3% (according to the current state-of-the-art) to 97.3%. While currently available computers do not allow a global optimization of the much larger metabolic network of E. coli, the main strengths of GlobalFit are already played out when considering only one growth and one non-growth case simultaneously. Application of a corresponding strategy halves the number of unexplained cases for the already highly curated E. coli model, increasing accuracy from 90.8% to 95.4%.
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