Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity

Abstract

The process of anaerobic digestion in which waste biomass is transformed to methane by complex microbial communities has been modeled for more than 16 years by parametric gray box approaches that simplify process biology and do not resolve intracellular microbial activity. Information on such activity, however, has become available in unprecedented detail by recent experimental advances in metatranscriptomics and metaproteomics. The inclusion of such data could lead to more powerful process models of anaerobic digestion that more faithfully represent the activity of microbial communities. We augmented the Anaerobic Digestion Model No. 1 (ADM1) as the standard kinetic model of anaerobic digestion by coupling it to Flux-Balance-Analysis (FBA) models of methanogenic species. Steady-state results of coupled models are comparable to standard ADM1 simulations if the energy demand for non-growth associated maintenance (NGAM) is chosen adequately. When changing a constant feed of maize silage from continuous to pulsed feeding, the final average methane production remains very similar for both standard and coupled models, while both the initial response of the methanogenic population at the onset of pulsed feeding as well as its dynamics between pulses deviates considerably. In contrast to ADM1, the coupled models deliver predictions of up to 1,000s of intracellular metabolic fluxes per species, describing intracellular metabolic pathway activity in much higher detail. Furthermore, yield coefficients which need to be specified in ADM1 are no longer required as they are implicitly encoded in the topology of the species’ metabolic network. We show the feasibility of augmenting ADM1, an ordinary differential equation-based model for simulating biogas production, by FBA models implementing individual steps of anaerobic digestion. While cellular maintenance is introduced as a new parameter, the total number of parameters is reduced as yield coefficients no longer need to be specified. The coupled models provide detailed predictions on intracellular activity of microbial species which are compatible with experimental data on enzyme synthesis activity or abundance as obtained by metatranscriptomics or metaproteomics. By providing predictions of intracellular fluxes of individual community members, the presented approach advances the simulation of microbial community driven processes and provides a direct link to validation by state-of-the-art experimental techniques.

Understanding methane emission from stored animal manure: A review to guide model development

“…Such an approach was attempted earlier by introducing 10 artificial microbial species within each functional group of the ADM1 and varying the specific maximum substrate utilization rate and the half saturation constant (Ramirez et al, 2009). Weinrich et al (2019) elaborated on this idea by using the unique metabolic potential of real methanogenic archaea species and predicted thousands of metabolic fluxes with flux-based analysis. Neither study considered the effect of temperature, which greatly influences methanogenic activity (Elsgaard et al, 2016) and hydrolysis rate (Donoso-Bravo et al, 2009).…”

The impact of crude glycerol from biodiesel production and its trace element content on biomethane production in a batch experiment: modelling as a step towards impartial routine comparison of results

“…Instead of simple comparisons of resulting biomethane production, the modeling approach can provide a step forward towards an impartial routine comparison of the results obtained from a larger number of experiments. The results of biomethane production curve models described in Table 4 can more readily be coupled and compared with results from other studies as was suggested only recently (Weinrich et al, 2019;Elagroudy et al, 2020). Significant differences between the models were also observed for maximum rate methane production, where the Transfer model's estimations were up to 196% higher than those of the logistic model.…”

Molecular Microbial Community Analysis as an Analysis Tool for Optimal Biogas Production

“…In addition, the flux-balance analysis of the methanogens actively was continuously updated in ADM1. As a result, this hybrid model could provide detailed information regarding the activity pathways in anaerobic digestion compared to the original ADM1 and could predict the intracellular activity of microbial species that are compatible with experimental data obtained from meta-genomic and meta-transcriptomic analysis [176].…”