Central carbon metabolism is a key component in the metabolic network of living organisms as these pathways harbor many of the most important mechanisms for energy biosynthesis, as well as producing the precursor compounds for most essential biomass building blocks. The energy production strategies defined in the central metabolic pathways have a significant impact on the behavior and growth conditions of microorganisms, thus playing a crucial role in the quantitative prediction of biomass and energy yields. Energy production strategies in microbes are highly diversified, unlike those in higher eukaryotes. These strategies primarily depend on environmental factors such as: (i) carbon source utilization; (ii) ability to respire by reducing numerous electron acceptors; and (iii) fermentation capabilities.
It continues to be challenging to make accurate computational predictions based on metabolic models and in silico simulations interpreting complex microbial behavior. Tools for automated metabolic model reconstruction such as ModelSEED can rapidly generate draft genome-scale metabolic models from annotated genome sequences. However, these draft models, and in some cases even curated published models, can lack accuracy in predicting growth yields, ATP production yields, and central carbon flux profiles. This poor accuracy stems primarily from three common problems: (i) poor representation of energy biosynthesis pathways; (ii) a lack of diverse electron transport chain (ETC) variations; and (iii) addition of extensive gapfilling reactions that can sometimes misrepresent an organism’s behavior.
Many of these problems can be avoided by using a simplified model comprised of only the most confidently annotated and biologically critical pathways for energy biosynthesis (see figure). We define these models as Core Metabolic Models (CMM), and they consist primarily of the sugar oxidation pathways, the fermentation pathways, and the ETC variations. We developed an approach for the reconstruction and analysis of CMMs based on annotated genome sequences, which we implemented as a pipeline in the DOE Systems Biology Knowledgebase (KBase). In this chapter, we demonstrate how this analysis workflow can be run in KBase. The pipeline is comprised of four main steps: (i) genome annotation by RAST ; (ii) CMM reconstruction; (iii) gapfilling; and (iv) flux balance analysis (FBA). We also discuss methods for exploring metabolic diversity by studying the variations in central metabolic pathways in a phylogenetic context.
Core model construction workflow Narrative
The core model construction workflow (along with data and commentary) described in the article is available as a Narrative–a reproducible workflow that you can copy to your own KBase account and rerun, perhaps changing some of the parameters or adding your own datasets.
You will need to register for a free KBase user account to view and run the core model construction Narrative.
Please visit the New to KBase page for general information about using KBase, or the Metabolic Modeling page for details about KBase’s modeling tools.
Edirisinghe J.N., Faria J.P., Harris N.L., Allen B.H., Henry C.S. (2018) Reconstruction and Analysis of Central Metabolism in Microbes. In: Fondi M. (eds) Metabolic Network Reconstruction and Modeling. Methods in Molecular Biology, vol 1716. Humana Press, New York, NY
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