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.
Key words
- Metabolic models
- Draft models
- Genome-scale reconstruction
- Flux balance analysis
- DOE knowledgebase
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Acknowledgments
This work is supported by the Office of Biological and Environmental Research's Genomic Science program within the US Department of Energy Office of Science, under award numbers DE-AC02-05CH11231, DE-AC02-06CH11357, DE-AC05-00OR22725, and DE-AC02-98CH10886.
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Allen, B.H., Gupta, N., Edirisinghe, J.N., Faria, J.P., Henry, C.S. (2022). Application of the Metabolic Modeling Pipeline in KBase to Categorize Reactions, Predict Essential Genes, and Predict Pathways in an Isolate Genome. In: Navid, A. (eds) Microbial Systems Biology. Methods in Molecular Biology, vol 2349. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1585-0_13
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