Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE[1] (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant E. coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid cycle and the novel BDO production route. We further analyzed these models to predict the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth and byproduct formation. We also identified targets for metabolic engineering for improved BDO production and yield, while taking into consideration other intracellular states. The conclusions of the performed analysis are consistent with the experimentally tested designs, and these results demonstrate the potential and effectiveness of ORACLE for the design of microbial cell factories. 1. Miskovic, L. & Hatzimanikatis, V. Production of biofuels and biochemicals: in need of an ORACLE. Trends in biotech 28, 391–7(2010).

Exploring enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

251st National Meeting (2016)

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