Genome‐scale metabolic modeling of a clostridial co‐culture for consolidated bioprocessing

Abstract

An alternative consolidated bioprocessing approach is the use of a co-culture containing cellulolytic and solventogenic clostridia. It has been demonstrated that the rate of cellulose utilization in the co-culture of Clostridium acetobutylicum and Clostridium cellulolyticum is improved compared to the mono-culture of C. cellulolyticum, suggesting the presence of syntrophy between these two species. However, the metabolic interactions in the co-culture are not well understood. To understand the metabolic interactions in the co-culture, we developed a genome-scale metabolic model of C. cellulolyticum comprising of 431 genes, 621 reactions, and 603 metabolites. The C. cellulolyticum model can successfully predict the chemostat growth and byproduct secretion with cellulose as the substrate. However, a growth arrest phenomenon, which occurs in batch cultures of C. cellulolyticum at cellulose concentrations higher than 6.7 g/L, cannot be predicted by dynamic flux balance analysis due to the lack of understanding of the underlying mechanism. These genome-scale metabolic models of the pure cultures have also been integrated using a community modeling framework to develop a dynamic model of metabolic interactions in the co-culture. Co-culture simulations suggest that cellobiose inhibition cannot be the main factor that is responsible for improved cellulose utilization relative to mono-culture of C. cellulolyticum.

Syntrophic metabolism of a co-culture containing Clostridium cellulolyticum and Rhodopseudomonas palustris for hydrogen production

“…These studies showed that co-culturing can improve H 2 production from sugars. However, there is still much to be understood about the key metabolic interactions and population dynamics in such co-cultures that are critical for improvement of syntrophic efficiency and H 2 productivity [20]. Continued improvements in our ability to track metabolite fluxes will provide a more comprehensive picture of intermediary metabolism and the factors that control the flux of organic matter that result in H 2 production.…”

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

“…Dynamic CBM CBM that replaces the steady-state assumption with a pseudosteady-state assumption to capture changes in the system or the environment over time (49)(50)(51)(52).…”

“…Dynamic CBMs describe bacterial growth in changing environments by relaxing the steady-state assumption of CBMs and instead assuming pseudo-steady states over short time periods (49)(50)(51)(52). CBMs have been coupled with gene expression data to probe metabolic changes in M. tuberculosis in response to hypoxia (53) or with spatial models of competing bacterial species to determine bacterial ecosystem dynamics (54).…”

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].…”