Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification.
Biotechnology and bioengineering
Wiley - V C H Verlag GmbbH & Co.
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Cankorur-Cetinkaya, A., Dikicioglu, D., & Oliver, S. (2017). Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification.. Biotechnology and bioengineering, 114 2605-2615. https://doi.org/10.1002/bit.26380
Genome-scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene-reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome-scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA and steroid biosynthetic processes, cellular alcohol metabolic process and oxidation-reduction process. Further, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development.
EC FP7 CP (289126)
External DOI: https://doi.org/10.1002/bit.26380
This record's URL: https://www.repository.cam.ac.uk/handle/1810/265261