Publicação
Improved in silico strain design by a combined approach to simulation and optimization
| Resumo: | Saccharomyces cerevisiae has been the organism of choice for producing a variety of compounds with diverse applications, from pharmaceuticals to biofuels. Often, however, the construction of a good producing host requires the insertion of heterologous genes, as well as the modification of the host metabolism to force the metabolic flux through the desired pathway. The application of rational in silico strain design tools allows to predict which genetic modifications are more likely to produce the desired impacts. This task, however, is a complex one, requiring the existence of reliable models and strain design tools. In this talk, several contributions to the field of strain design will be presented, including tools for improving the prediction of metabolic flux distributions by the incorporation of different types of data and knowledge, as well as different optimization methods able to find designs with partial or total coupling between product and biomass production. Finally, a novel simulation method will be presented, the Turnover Dependent Phenotypic Simulation (TDPS), which was designed with the goal of simulating quantitatively the phenotype of strains with diverse genetic modifications in a resource conscious manner. Besides gene deletions and down-regulations, TDPS can also simulate the up-regulation of metabolic reactions as well as the introduction of heterologous genes or the activation of dormant reactions. TDPS was validated using metabolically engineered S. cerevisiae strains available in the literature by comparing the simulated and experimental production yields of the target metabolite. |
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| Autores principais: | Rocha, I. |
| Ano: | 2015 |
| País: | Portugal |
| Tipo de documento: | outro |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | Saccharomyces cerevisiae has been the organism of choice for producing a variety of compounds with diverse applications, from pharmaceuticals to biofuels. Often, however, the construction of a good producing host requires the insertion of heterologous genes, as well as the modification of the host metabolism to force the metabolic flux through the desired pathway. The application of rational in silico strain design tools allows to predict which genetic modifications are more likely to produce the desired impacts. This task, however, is a complex one, requiring the existence of reliable models and strain design tools. In this talk, several contributions to the field of strain design will be presented, including tools for improving the prediction of metabolic flux distributions by the incorporation of different types of data and knowledge, as well as different optimization methods able to find designs with partial or total coupling between product and biomass production. Finally, a novel simulation method will be presented, the Turnover Dependent Phenotypic Simulation (TDPS), which was designed with the goal of simulating quantitatively the phenotype of strains with diverse genetic modifications in a resource conscious manner. Besides gene deletions and down-regulations, TDPS can also simulate the up-regulation of metabolic reactions as well as the introduction of heterologous genes or the activation of dormant reactions. TDPS was validated using metabolically engineered S. cerevisiae strains available in the literature by comparing the simulated and experimental production yields of the target metabolite. |
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