(568f) Towards a Metabolic and Expression Model of the Metabolically Versatile Bacterium Rhodopseudomonas Palustris
AIChE Annual Meeting
Wednesday, October 31, 2018 - 5:00pm to 5:18pm
Genome scale metabolic models provide a systems-level approach to studying an organismâs metabolism based on the conservation of mass under steady state conditions. However, a major limitation of metabolic models (M-Models) is the inability to directly incorporate macromolecular synthesis, and therefore omics data, into the model. To overcome these shortcomings, metabolic and expression (ME-Models)  have recently been developed to account for the synthesis of the macromolecular machinery of living systems. Similar to M-Models, ME-Models make use of optimization to predict optimal functional states based on the given objective function that the organism is assumed to optimize in vivo. Where ME-Models surpass M-Models is in the stringency of the constraints being implemented, as they impose the added costs of synthesizing and replicating the cellâs macromolecular machinery. Furthermore, the addition of transcription and translation âreactionsâ makes it possible to quantitatively simulate expression rates and therefore allows for the incorporation of omics data into the formulation. However, this added complexity is usually accompanied by an increase in the amount and type of experimental data required, which is problematic when modeling non-model organisms.
In this study, a genome-scale metabolic model was first generated from available databases, including KBase, Uniprot, and KEGG. The reactions were then checked and corrected for imbalances in elemental mass and charge. Next, missing functionalities in the model were reconciled using optimization tools, such as GapFill . The metabolic model currently contains 925 reactions, 1220 metabolites, and 1134 genes. An expression matrix (E-matix), containing transcription, translation, and post-transcriptional modifications , is currently being reconstructed. After the reconstruction of the expression matrix, the two networks will be integrated  and previously established â will be implemented. These constraints link the synthesis of macromolecular molecules to catalysis of metabolic components and to biomass production. ME-Model results obtained will be compared with those from the metabolic model to quantify the change in solution space caused by the additional constraints. This ME model will enable more accurate genetic intervention predictions which maximize production of industrially relevant products from R. palustris. This predictive power will continue to grow as more omics datasets become available, facilitating the establishment of this metabolically versatile, non-model microorganism as a biotechnology platform.
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