Development of a Theoretical Workflow for Metabolic Engineering and its Application to Terpenoid Production in Escherichia coli (2015-2018)
Metabolic Engineering is an emerging science aiming at the development of cellular factories for the overproduction of valuable chemicals. Target molecules can be either naturally produced by endogenous metabolic pathways, or the host metabolism should be complemented by a non-inherent metabolic pathway to enable their heterologous production. Over the past decades, mathematical modeling of cellular metabolism for strain and process optimization has given rise to a more rational, model-based Metabolic Engineering science. However, theoretical workflows providing advice on limitations and proper application of the vast number of available mathematical tools are still scarce. Initially, we review the application of mathematical methods to increase the production of succinate in engineered strains. Succinate is an important building block whose biotechnological production has gained much attention in the last decade. From this initial work, we conclude that direct experimental implementation of model predictions (in silico knowledge) is not a straightforward process yet. One of the many reasons is the intrinsic complexity of living systems, which cannot be fully captured by the simplicity of widely used stoichiometric models of metabolism. Additionally, incongruences in the modeling process and the reporting of experimental results hamper a proper assessment of the prediction power of current modeling approaches.
Motivated by these observations, we developed a theoretical workflow for metabolic engineering, highlighting capabilities and limitations of each method. The workflow considers the application of not only constraint-based methods like Flux Balance Analysis (FBA), which have been traditionally used to understand optimality principles shaping bacterial metabolism, but also of kinetic-based methods whose spread has been hindered so far by limitations related to model parametrization and to high demand on computational power required to analyze genome-scale kinetic models. While developing the workflow, we paid special attention to consider the so-called metabolic burden, a phenomenon presented in "loaded" cells and characterized by the reduction of both biomass yield and critical growth rate for acetate secretion. The suggested protocol was mainly applied to generate in silico knowledge, aimed to guide future experimental efforts towards optimization of taxadiene production in Escherichia coli (E. coli) at the strain and process level. Taxadiene is a precursor molecule for the anticancer drug taxol and its biotechnological production has gained much attention due to the low yields of the traditional extraction process from the bark of the pacific yew tree. During the development of a flexible taxadiene producing strain, simultaneous utilization of glucose and xylose by E. coli was also analyzed. By applying various tools described in the protocol, metabolic load and effects arising from simultaneous sugar uptake were assessed, especially focusing on the production potential of each strain. This analysis should allow the selection of strain candidates for further optimization.
Former project supervisor: Dr. Miguel Angel Valderrama Gomez