ArticlePloS one2020
Defining the nutritional input for genome-scale metabolic models: A roadmap.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Characterizing Spatiotemporal Expansion of Pseudomonas aeruginosa Communities in Polymer and Mucin Gel Environments.Biotechnology and bioengineering · 2026Article
- Metabolic modeling unveils potential probiotic roles ofISME communications · 2026Article
- Viral lysis and host reprogramming impact carbohydrate, amino acid, and osmolyte cycling in salt-marsh tidal creek sediments.ISME communications · 2026Article
- From Genome to Function: Metabolism-Wide Models of Lifecycle Stages of Parasites.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Metatranscriptomics-based metabolic modeling of patient-specific urinary microbiome during infection.NPJ biofilms and microbiomes · 2025Article
- Personalized Clostridioides difficile colonization risk prediction and probiotic therapy assessment in the human gut.Cell systems · 2025Article
- Multi-omics analysis highlights the link of aging-related cognitive decline with systemic inflammation and alterations of tissue-maintenance.bioRxiv : the preprint server for biology · 2025Article
- Metabolic modeling elucidates phenformin and atpenin A5 as broad-spectrum antiviral drugs against RNA viruses.Communications biology · 2025Article
- Moving from genome-scale to community-scale metabolic models for the human gut microbiome.Nature microbiology · 2025Review
- Metabolic modelling reveals the aging-associated decline of host-microbiome metabolic interactions in mice.Nature microbiology · 2025Article
- Influence of Amino Acids on Quorum Sensing-Related Pathways inMetabolites · 2025Article
- Genome-scale metabolic modelling reveals interactions and key roles of symbiont clades in a sponge holobiont.Nature communications · 2024Article
- Targeted isolation of Methanobrevibacter strains from fecal samples expands the cultivated human archaeome.Nature communications · 2024Article
- Community metabolic modeling of host-microbiota interactions through multi-objective optimization.iScience · 2024Article
- Metabolic model predictions enable targeted microbiome manipulation through precision prebiotics.Microbiology spectrum · 2024Article
- Changes in Bacterial Gut Composition in Parkinson's Disease and Their Metabolic Contribution to Disease Development: A Gut Community Reconstruction Approach.Microorganisms · 2024Article
- Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model.Nature microbiology · 2024Article
- Predicting microbial interactions with approaches based on flux balance analysis: an evaluation.BMC bioinformatics · 2024Article
- Unveiling metabolic pathways involved in the extreme desiccation tolerance of an Atacama cyanobacterium.Scientific reports · 2023Article
- Transcriptome-guided metabolic network analysis reveals rearrangements of carbon flux distribution inmSystems · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The reconstruction and application of genome-scale metabolic network models is a central topic in the field of systems biology with numerous applications in biotechnology, ecology, and medicine. However, there is no agreed upon standard for the definition of the nutritional environment for these models. The objective of this article is to provide a guideline and a clear paradigm on how to translate nutritional information into an in-silico representation of the chemical environment. Step-by-step procedures explain how to characterise and categorise the nutritional input and to successfully apply it to constraint-based metabolic models. In parallel, we illustrate the proposed procedure with a case study of the growth of Escherichia coli in a complex nutritional medium and show that an accurate representation of the medium is crucial for physiological predictions. The proposed framework will assist researchers to expand their existing metabolic models of their microbial systems of interest with detailed representations of the nutritional environment, which allows more accurate and reproducible predictions of microbial metabolic processes.
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