Browsing by Author "Tenkanen, Tuula"

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    Biotekniikan hyödyntämismahdollisuus palkokasvipohjaisten lihaa korvaavien tuotteiden valmistuksessa
    (2016-12-05) Tenkanen, Tuula; Eerikäinen, Tero; Kemiantekniikan korkeakoulu; Rautkari, Lauri
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    Metabolic model guided design enabling adaptively evolving improved production by microbial hosts
    (2021-06-15) Tenkanen, Tuula; Jouhten, Paula; Kemian tekniikan korkeakoulu; Penttilä, Merja
    Engineering microbial hosts to achieve higher production levels of e.g. chemicals is challenging as genotype-phenotype relationships are limitedly known. Adaptive laboratory evolution allows host improvement without the need to know the genotypic underpinnings of phenotypes. However, adaptive laboratory evolution can only be used when the desired production related trait is genetically correlated with fitness (i.e. growth), and chemical production is rarely naturally growth-coupled. Metabolic network creates couplings between fluxes, including biosynthetic fluxes for growth. Besides the metabolic network structure, also the nutritional environment affects the flux couplings, and these can be predicted using genome-scale metabolic model simulations. In this work, a novel algorithm EvolveXGA that use genome-scale metabolic models was demonstrated for designing strategies combining biochemical reaction deletions (i.e. achievable with gene deletions) and nutritional environments to optimize target flux(es) coupling with growth. When the target fluxes are chosen to include either the production pathway or reactions providing precursors for the desired pathway, the strategies are predicted to enable adaptively evolving improved production. EvolveXGA uses a genetic algorithm as an optimization routine. First, the genetic algorithm was set up for optimizing the target flux(es) coupling to growth using reaction deletions and nutritional environment as variables. Then, EvolveXGA was tested for designing strategies including reaction deletions and nutritional environment to growth-couple glycolic acid production pathway in Saccharomyces cerevisiae. Finally, the generalizability of the method was evaluated by designing such strategies for 28 other heterologous products. EvolveXGA found strategies to couple 13 of these production pathways with growth including the oxalate pathway for glycolic acid. In addition, for the remainder of the pathways EvolveXGA found strategies to couple native precursor providing reactions with growth. Some of these strategies showed potential to be realized in vivo. Synthetic biology tools have enabled fast development and implementation of new production pathways into microbial hosts. However, improving the production experimentally from lab demonstration to economically feasible levels has remained resource-intensive and creates a bottleneck for new industrial microbial fermentation processes. The novel model-guided approach that was demonstrated in silico in this project offers generalizable means to accelerate the production improvement using operationally simple, powerful, adaptive laboratory evolution.