ArticlePLoS computational biology2019
BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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60 citing papers in PubMed.
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- From Genome to Function: Metabolism-Wide Models of Lifecycle Stages of Parasites.Methods in molecular biology (Clifton, N.J.) · 2026Article
- ModellingCurrent research in microbial sciences · 2026Article
- Optimization-based framework with flux balance analysis (FBA) and metabolic pathway analysis (MPA) for identifying metabolic objective functions.PLoS computational biology · 2025Article
- Moving from genome-scale to community-scale metabolic models for the human gut microbiome.Nature microbiology · 2025Review
- MIRO1 mutation leads to metabolic maladaptation resulting in Parkinson's disease-associated dopaminergic neuron loss.NPJ systems biology and applications · 2025Article
- Metabolic Objectives and Trade-Offs: Inference and Applications.Metabolites · 2025Review
- Inferring metabolic objectives and trade-offs in single cells during embryogenesis.Cell systems · 2025Article
- Implementation of aComputational and structural biotechnology journal · 2025Article
- Insights into optimization of oleaginous fungi - genome-scale metabolic reconstruction and analysis ofComputational and structural biotechnology journal · 2025Article
- Determination of the genome-scale metabolic network ofFrontiers in bioengineering and biotechnology · 2025Article
- BioModTool: from biomass composition data to structured biomass objective functions for genome-scale metabolic models.Bioinformatics advances · 2025Article
- Metabolic modeling of host-microbe interactions.Computational and structural biotechnology journal · 2025Review
- An automated network-based tool to search for metabolic vulnerabilities in cancer.Nature communications · 2024Article
- Mixotrophic growth of a ubiquitous marine diatom.Science advances · 2024Article
- Flux sampling in genome-scale metabolic modeling of microbial communities.BMC bioinformatics · 2024Article
- Predicting microbial interactions with approaches based on flux balance analysis: an evaluation.BMC bioinformatics · 2024Article
- Genome-scale model development and genomic sequencing of the oleaginous cladeFrontiers in bioengineering and biotechnology · 2024Article
- Genome-scale flux balance analysis reveals redox trade-offs in the metabolism of the thermoacidophileFrontiers in systems biology · 2024Article
- Microbiome modeling: a beginner's guide.Frontiers in microbiology · 2024Review
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11 authors.
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Abstract
Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).
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