A strategy for the computational metabolic re-engineering of yeast to maximize the production of glycerol and glycerol-3-phosphate.

Shubham Jain, James Smith, Marc-Thorsten Hütt. Computational Systems Biology Research Group, School of Engineering and Sciences, Jacobs University Bremen, D-28759 Bremen, Germany. Metabolic pathways of many microorganisms are often re-engineered for the production of improved yields in the biotechnology industry. Molecular bioengineering involves altering individual enzyme activities and associated reaction pathways. This work is a pilot computational study that aims to propose feasible metabolic re-engineering strategies. Constraint-based analysis is used to examine metabolic modifications in a recent model of Saccharomyces cerevisiae to increase the production and export of glycerol and its associated intermediate glycerol-3-phosphate (G3P). Ideally, the approaches for predicting the changes of chosen metabolic intermediates must consider apposing (neighbouring) reaction pathways under different metabolic responses. With respect to glycerol metabolism, there is aerobic utilisation into essential lipid biomass components (for example acyl dihydroxyacetone phosphate pathways for glycerolipids and the subcellular membrane glycerophospholipids) and the anaerobic anabolic pathway. Glycerol also functions as a compatible solute in osmoregulation in osmo-tolerant yeast strains that are capable of growing in high sugar or salt environments therefore any pathways associated with glycerol osmoregulation under ionic changes are important additional features of its metabolism. Under normal conditions free glycerol makes up a non-significant contribution to biomass. Steady state flux balance analysis (FBA) using the constraint-based reconstruction and analysis (COBRA) toolbox[1] is used for the prediction of different objectives under a range of media conditions. The inputs are ranges of nutrient utilisation rates (fluxes) of minimal and rich media; outputs are fluxes chosen as objectives and include glycerol and G3P production compared to biomass. Additionally, this work uses OPTFLUX[2], to identify viable gene deletions, for the optimization of fluxes in desired phenotypes. In this case, the objective function is the biomass product-coupled yield for glycerol and G3P. 1. Becker et al (2007). Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox. Nature Protocols 2, 727-738. 2. Rocha et al (2010). OptFlux: an open-source software platform for in silico metabolic engineering. BMC Systems Biology 4:45.

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