Metabolic modeling of a mutualistic microbial community

The rate of production of methane in many environments depends upon mutualistic interactions between sulfate-reducing bacteria and methanogens. To enhance our understanding of these relationships, we took advantage of the fully sequenced genomes of Desulfovibrio vulgaris and Methanococcus maripaludis to produce and analyze the first multispecies stoichiometric metabolic model. Model results were compared to data on growth of the co-culture on lactate in the absence of sulfate. The model accurately predicted several ecologically relevant characteristics, including the flux of metabolites and the ratio of D. vulgaris to M. maripaludis cells during growth. In addition, the model and our data suggested that it was possible to eliminate formate as an interspecies electron shuttle, but hydrogen transfer was essential for syntrophic growth. Our work demonstrated that reconstructed metabolic networks and stoichiometric models can serve not only to predict metabolic fluxes and growth phenotypes of single organisms, but also to capture growth parameters and community composition of simple bacterial communities

Engineering improved productivity of 1,4-butanediol in E. coli – a kinetic modeling approach

Microbial cell factories are becoming a norm for commercially viable production of chemicals for pharmaceutical, biotechnology, food and beverage industries. However, engineering of microbial cell factories requires a simultaneous optimization of several criteria such as productivity, yield, titer, stress tolerance, all the while retaining the efficient, cost-effective and robust process. One of the most prominent examples where a rational metabolic engineering strategy played a key role is in the production of 1,4-butanediol (BDO) in E. coli. In this study, we used the ORACLE (Optimization and Risk Analysis of Complex Living Entities) framework to analyze possible enhancements of the E. coli strain engineered for improved production of BDO. ORACLE framework allowed us to integrate thermodynamics, available omics and kinetic data into a population of large-scale kinetic models. Analysis of the engineered E. coli strain led to the identification of three critical modules within the metabolic network which contained the enzymes that primarily control the fluxes leading to BDO production. The enzymes in these modules are focused around: a) central glycolysis, b) the lower branch of tricarboxyclic acid cycle, and c) novel BDO production route. However, the manipulation of the enzymes in the identified modules - while possibly leading to the increased BDO production - had complex effects on other intracellular states like redox state, energy charge, cofactor levels, cellular growth and byproduct formation. We used the large-scale kinetic models generated by ORACLE to postulate successfully metabolic engineering alternatives for optimal performance with reduced byproduct secretion and fine-tuned redox balance, energy charge and cofactor levels. While in the current study, the aim was to improve BDO production, the methodology presented here can readily be applied to other products and organisms of interest