ArticleiScience2021
Importance of the biomass formulation for cancer metabolic modeling and drug prediction.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Cancer-associated fibroblasts drive metabolic heterogeneity in colorectal cancer cells: predictions from metabolic modeling.NPJ systems biology and applications · 2026Article
- Metabolic Objectives and Trade-Offs: Inference and Applications.Metabolites · 2025Review
- An automated network-based tool to search for metabolic vulnerabilities in cancer.Nature communications · 2024Article
- Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas.Briefings in bioinformatics · 2024Article
- Response score-based protein structure analysis for cancer prediction aided by the Internet of Things.Scientific reports · 2024Article
- COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery.Briefings in bioinformatics · 2023Article
- Mathematical reconstruction of the metabolic network in an in-vitro multiple myeloma model.PLoS computational biology · 2023Article
- Metabolic modelling-based in silico drug target prediction identifies six novel repurposable drugs for melanoma.Cell death & disease · 2023Article
- Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures.Metabolites · 2023Review
- Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems.Computational and structural biotechnology journal · 2023Review
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Authors and funding
5 authors.
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Abstract
Genome-scale metabolic reconstructions include all known biochemical reactions occurring in a cell. A typical application is the prediction of potential drug targets for cancer treatment. The precision of these predictions relies on the definition of the objective function. Generally, the biomass reaction is used to illustrate the growth capacity of a cancer cell. Today, seven human biomass reactions can be identified in published metabolic models. The impact of these differences on the metabolic model predictions has not been explored in detail. We explored this impact on cancer metabolic model predictions and showed that the metabolite composition and the associated coefficients had a large impact on the growth rate prediction accuracy, whereas gene essentiality predictions were mainly affected by the metabolite composition. Our results demonstrate the importance of defining a consensus biomass reaction compatible with most human models, which would contribute to ensuring the reproducibility and consistency of the results.
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