Optimization of the Production of Butanol in Recombinant Pseudomonas Putida Using Large-Scale Kinetic Models
The production of valuable biofuels and biochemicals by conversion of renewable resources is a basis of sustainable economy. Heterologous expression of biosynthetic pathways taken from natural producers or expression of de novo synthetic pathways into microbial work horses such as E. coli or S. cerevisiae allows for production of a wide spectra of biofuels and biochemicals. P. putida has emerged as one of the amenable production hosts with a number of advantages over natural producers. P. putida is a non-pathogenic soil bacterium known for its versatile metabolism and its tolerance to high toxicity compounds. For instance, it is reported that P. putida can grow in the presence of high concentrations of butanol1. This highly adaptive bacterium has been found to survive and grow on a wide range of substrates from pure caffeine to toxic industrial waste.
In this contribution, we devised metabolic engineering strategies for improving butanol production in recombinant P. putida strain using large-scale kinetic models. Large-scale kinetic models allowed us to simultaneously optimize several factors such as specific productivity and yield. We started by embedding the butanol biosynthetic pathway2 in the genome-scale model of P. putida and then we derived a consistently reduced, core stoichiometric model that was consistent with its genome-scale counterpart. We then used the ORACLE3,4,5 (Optimization and Risk Analysis of Complex Living Entities) framework to integrate available experimental information and information from literature and databases to build a population of large-scale kinetic models of recombinant P. putida producing butanol. We used these models to identify the enzymes with the highest impact on the production of butanol and we proposed metabolic engineering strategies for improved specific productivity and yield.
This work demonstrates the potential and usefullness of ORACLE as a framework for optimizing production of biofuels and biochemicals.
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