Constraint-based metabolic models and their application in industrial biotechnology
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This thesis is focused on the application of small constraint-based models to analyze and predict the behavior of wild type and modified strains of Pichia pastoris. The presented work deals with the common limitations that industrial environment imposes: measurements are scarce, models are not detailed, the modelled organisms are not always well-known and, in most cases, they are genetically modified.
The results have been divided in three articles.
The first presents the validation of a small FBA (flux balance analysis) model of unmodified P. pastoris cells, based on the assumption of “maximizing growth” as evolved biological objective for the cells. The model has been validated in heterogeneous experimental situations.
In the second article, I exploit a feature of constraint-based models: they are easily extendable.In particular, the FBA model has been extended to represent and predict the behavior of genetically modified cells of P. pastoris producing a recombinant protein. The new model represents the energetic requirements of the protein production process, and also the impact that protein production has over the cells growth. The model predictions for growth and even for protein production have been validated against multiple experimental datasets.
Finally, a software toolbox is presented. It implements two MFA-wise methods to get estimations from small, constraint-based models in uncertain scenarios. These implementations simplify and extend the application of MFA (Metabolic flux analysis) when measurements are scarce and imprecise. The thesis is an application of small, constraint-based models to P. pastoris. It illustrates how these models can be a valuable tool to analyze, estimate or predict the behavior of unmodified and modified P. pastoris cells. The approaches followed in this work account for some of the limitations of industrial environments, and thus, they may be of use when modelling other microorganisms of industrial interest.
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