ArticleMethods in molecular biology (Clifton, N.J.)2022
Application of the Metabolic Modeling Pipeline in KBase to Categorize Reactions, Predict Essential Genes, and Predict Pathways in an Isolate Genome.
Article in Methods in molecular biology (Clifton, N.J.), 2022. 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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10 citing papers in PubMed.
- Modeling neutrophilic iron-oxidizing bacteria: a KBase narrative resource describing newly integrated metabolic reactions.Microbiology resource announcements · 2026Article
- Identifying dormancy-associated enzymes in Mycobacterium tuberculosis through a computational pipeline integrating flux balance analysis and metabolic modeling.Molecular diversity · 2026Article
- Genomic and chemical insights into a human lectin-binding extracellular polysaccharides from Parageobacillus toebii strain H-70.PloS one · 2026Article
- Production, characterization, and genomic insights of a human lectin-binding exopolysaccharide from Haloarcula japonica strain SST1.Scientific reports · 2025Article
- Enhancing Soil Health and Corn Productivity with a Co-Fermented Microbial Inoculant (CFMI-8): A Field-Based Evaluation.Microorganisms · 2025Article
- Genetic- and culture-based tools for studyingMicrobiology resource announcements · 2025Article
- Article
- Kinetics-based inference of environment-dependent microbial interactions and their dynamic variation.mSystems · 2024Article
- A Brief History of Microbial Study and Techniques for Exploring the Gastrointestinal Microbiome.Clinics in colon and rectal surgery · 2023Review
- Draft Genome Sequence of a Polyhydroxyalkanoate-Producing Bacillus cereus Strain Isolated from Nuevo Leon State, Mexico.Microbiology resource announcements · 2022Article
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5 authors.
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Abstract
The DOE Systems Biology Knowledgebase (KBase) platform offers a range of powerful tools for the reconstruction, refinement, and analysis of genome-scale metabolic models built from microbial isolate genomes. In this chapter, we describe and demonstrate these tools in action with an analysis of isoprene production in the Bacillus subtilis DSM genome. Two different methods are applied to build initial metabolic models for the DSM genome, then the models are gapfilled in three different growth conditions. Next, flux balance analysis (FBA) and flux variability analysis (FVA) techniques are applied to both study the growth of these models in minimal media and classify reactions within each model based on essentiality and functionality. The models are applied with the FBA method to predict essential genes, which are then compared to an updated list of essential genes obtained for B. subtilis 168, a very similar strain to the DSM isolate. The models are also applied to simulate Biolog growth conditions, and these results are compared with Biolog data collected for B. subtilis 168. Finally, the DSM metabolic models are applied to explore the pathways and genes responsible for producing isoprene in this strain. These studies demonstrate the accuracy and utility of models generated from the KBase pipelines, as well as exploring the tools available for analyzing these models.
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