Conventional modeling of bioprocesses is based on descriptive empirical models in which the functional bacteria are viewed as "black-boxes" without further consideration of the intrinsic intracellular processes. Due to the lack of a mechanistic foundation, such empirical bioprocess models cannot be generally applied across different cases and need to be calibrated routinely. Recent advances in high-throughput technologies, such as genomics, transcriptomics, and others, have enabled the development of genome-scale metabolic models (GEMs), which are targeted at cellular function on a quantitative basis and provide insight into the rationales underlying the metabolic behaviors of functional bacteria. Despite their utility, construction of high-quality GEM is a time-consuming and labor-inte...[ Read more ]

Conventional modeling of bioprocesses is based on descriptive empirical models in which the functional bacteria are viewed as "black-boxes" without further consideration of the intrinsic intracellular processes. Due to the lack of a mechanistic foundation, such empirical bioprocess models cannot be generally applied across different cases and need to be calibrated routinely. Recent advances in high-throughput technologies, such as genomics, transcriptomics, and others, have enabled the development of genome-scale metabolic models (GEMs), which are targeted at cellular function on a quantitative basis and provide insight into the rationales underlying the metabolic behaviors of functional bacteria. Despite their utility, construction of high-quality GEM is a time-consuming and labor-intensive process, which limits their application to only a few well-understood model organisms. To address this challenge, an accurate and efficient approach for constructing multiple GEMs with reduced complexity has been proposed in this study and applied for quantitative investigation of the versatile metabolism of two key anaerobes, methanogenic archaea (MA) and sulfate-reducing prokaryotes (SRPs ), as well as their metabolic interactions.

Firstly, a GEM of the aceticlastic MA Methanosaeta concilii has been constructed and then validated with experimental data. Flux balance analysis (FBA) of the model accurately predicts experimental growth and gene knockout data with 93% accuracy. Furthermore, by comparing with previous published metabolic models of hydrogenotrophic MA, fundamental differences between two metabolic types of methanogenesis, hydrogenotrophic and methylotrophic, have been elucidated, with implications for metabolic versatility and the potential for engineering of MA to utilize new substrates.

An accurate and efficient approach for constructing metabolic models of multiple strains has been further developed by combining comparative genomics and core model concept, and then applied to 24 sulfate-reducing prokaryotes (SRPs) belonging to three distinct genera. The reference model of the well-studied model SRP Desulfovibrio vulgaris Hildenborough (DvH) is validated via FBA. The DvH model predictions match reported experimental growth and energy yields, demonstrating that the metabolic model with reduced complexity works successfully. Further, steady-state simulation of SRP metabolic models under different growth conditions shows how the use of different electron transfer pathways leads to energy generation. Three energy conservation mechanisms are identified, including menaquinone-based redox loop, hydrogen cycling, and proton pumping. Flavin-based electron bifurcation (FBEB) is also demonstrated to be an essential mechanism for supporting energy conservation.

Based on the single-species models of SRPs and MA, a metabolic model of the microbial community consisting of a SRP species, D. vulgaris, and two MA species, M. maripaludis, and M. barkeri, has been further developed. SRPs and MA always coexist as microbial communities in a variety of anaerobic environments. Their competitive and cooperative interactions influence the composition and function of the microbial community. The community model is applied to evaluate how these two conflicting forces shape the microbial community under different sulfate levels. Model predictions on optimal community compositions under different sulfate levels agree well with experimental data from different sources. As expected, tri-cultures of three species in the absence of sulfate exhibits highest methane production. With an increasing availability of sulfate, system stability and productivity decrease despite the continued presence of acetate, which is due to a shift in the metabolism of these methanogens towards co-utilization of hydrogen with acetate. These findings indicate the important role of hydrogen dynamics in the stability and productivity of syntrophic communities.

Overall, the present work develops an accurate and efficient approach for constructing metabolic models of multiple organisms, which can be applied to other critical microbes in environmental and industrial systems, thereby enabling the quantitative prediction of their metabolic behaviors to benefit relevant applications.