Cetinkaya2017 - Engineering targets for Komagataella phaffii

Cetinkaya2017 - Engineering targets for
Komagataella phaffii

This model is described in the article:

Metabolic modelling to
identify engineering targets for Komagataella phaffii: The
effect of biomass composition on gene target identification

Ayca Cankorur-Cetinkaya, Duygu
Dikicioglu, Stephen G. Oliver
Biotechnology and Bioengineering
Abstract:

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.

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