Dynamic flux balance modeling of S. cerevisiae and E. coli co-cultures for efficient consumption of glucose/xylose mixtures

Abstract

Current researches into the production of biochemicals from lignocellulosic feedstocks are focused on the identification and engineering of individual microbes that utilize complex sugar mixtures. Microbial consortia represent an alternative approach that has the potential to better exploit individual species capabilities for substrate uptake and biochemical production. In this work, we construct and experimentally validate a dynamic flux balance model of a Saccharomyces cerevisiae and Escherichia coli co-culture designed for efficient aerobic consumption of glucose/xylose mixtures. Each microbe is a substrate specialist, with wild-type S. cerevisiae consuming only glucose and engineered E. coli strain ZSC113 consuming only xylose, to avoid diauxic growth commonly observed in individual microbes. Following experimental identification of a common pH and temperature for optimal co-culture batch growth, we demonstrate that pure culture models developed for optimal growth conditions can be adapted to the suboptimal, common growth condition by adjustment of the non-growth associated ATP maintenance of each microbe. By comparing pure culture model predictions to co-culture experimental data, the inhibitory effect of ethanol produced by S. cerevisiae on E. coli growth was found to be the only interaction necessary to include in the co-culture model to generate accurate batch profile predictions. Co-culture model utility was demonstrated by predicting initial cell concentrations that yield simultaneous glucose and xylose exhaustion for different sugar mixtures. Successful experimental validation of the model predictions demonstrated that steady-state metabolic reconstructions developed for individual microbes can be adapted to develop dynamic flux balance models of microbial consortia for the production of renewable chemicals.

Genome-Scale Reconstruction of the Metabolic Network in Oenococcus oeni to Assess Wine Malolactic Fermentation

“…These might include determination of biomass composition, exhaustive electronic and proton balancing of stoichiometric equations and integration of transcriptomic and proteomic data. On the other hand, dynamic flux balance analysis (dFBA) has emerged as a promising strategy to study batch cultures of several strains (Sainz et al, 2003; Hanly et al, 2012; Sánchez et al, 2014). Indeed, this methodology has been already applied to understand the behavior of industrial Saccharomyces cerevisiae strains in wine-like medium (Vargas et al, 2011).…”

In silico analysis of bioethanol production from glucose/xylose mixtures during fed-batch fermentation of co-culture and mono-culture systems

“…Genome-scale dFBA model of S. cerevisiae (wild-type and recombinant strain RWB218) was used for investigating fed-batch bioethanol production in terms of dynamic metabolic engineering studies focusing on gene insertion and deletion [17,18]. The superiority of co-culture system of substrate-selective microbes (wild-type S. cerevisiae and engineered E. coli strain ZSC113) over mono-culture (recombinant S. cerevisiae strain RWB218) in improving bioethanol production from glucose/xylose mixtures during batch fermentation is reported by Hanly and Henson [19] and Hanly et al [20]. The benefit of batch co-culture of Scheffersomyces stipitis and respiratory deficient S. cerevisiae for bioethanol production from glucose/xylose mixtures has also been reported [21].…”

Constructing and Analyzing Metabolic Flux Models of Microbial Communities

“…Modeling these communities can allow for rapid pre-screening of possible consortia, nutrient compositions, and metabolic engineering approaches to increase the efficiency and productivity of the microbial consortia [56]. Several studies modeling co-culture microbial communities using dFBA have been presented in literature [54,[57][58][59].…”