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In this paper we study a model of interacting dark energy - dark matter where the ratio between these components is not constant, changing from early to late times in such a way that the model can solve or alleviate the cosmic coincidence problem (CP). The interaction arises from an assumed relation of the form $\rho_x \propto \rho_d^\alpha$, where $\rho_x$ and $\rho_d$ are the energy densities of dark energy and dark matter components, respectively, and $\alpha$ is a free parameter. For a dark energy equation of state parameter $w=-1$ we found that, if $\alpha=0$, the standard $\Lambda$CDM model is recovered, where the coincidence problem is unsolved. For $0, Comment: 22 pages, 6 figures, revised version, with Bayesian Information Criterion (BIC) analysis included. Version published

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.

Background Methane yield and biogas productivity of biogas plants (BGPs) depend on microbial community structure and function, substrate supply, and general biogas process parameters. So far, however, relatively little is known about correlations between microbial community function and process parameters. To close this knowledge gap, microbial communities of 40 samples from 35 different industrial biogas plants were evaluated by a metaproteomics approach in this study. Results Liquid chromatography coupled to tandem mass spectrometry (Orbitrap Elite™ Hybrid Ion Trap-Orbitrap Mass Spectrometer) of all 40 samples as triplicate enabled the identification of 3138 different metaproteins belonging to 162 biological processes and 75 different taxonomic orders. The respective database searches were performed against UniProtKB/Swiss-Prot and seven metagenome databases. Subsequent clustering and principal component analysis of these data allowed for the identification of four main clusters associated with mesophile and thermophile process conditions, the use of upflow anaerobic sludge blanket reactors and BGP feeding with sewage sludge. Observations confirm a previous phylogenetic study of the same BGP samples that was based on 16S rRNA gene sequencing by De Vrieze et al. (Water Res 75:312–323, 2015). In particular, we identified similar microbial key players of biogas processes, namely Bacillales, Enterobacteriales, Bacteriodales, Clostridiales, Rhizobiales and Thermoanaerobacteriales as well as Methanobacteriales, Methanosarcinales and Methanococcales. For the elucidation of the main biomass degradation pathways, the most abundant 1 % of metaproteins was assigned to the KEGG map 1200 representing the central carbon metabolism. Additionally, the effect of the process parameters (i) temperature, (ii) organic loading rate (OLR), (iii) total ammonia nitrogen (TAN), and (iv) sludge retention time (SRT) on these pathways was investigated. For example, high TAN correlated with hydrogenotrophic methanogens and bacterial one-carbon metabolism, indicating syntrophic acetate oxidation. Conclusions This is the first large-scale metaproteome study of BGPs. Proteotyping of BGPs reveals general correlations between the microbial community structure and its function with process parameters. The monitoring of changes on the level of microbial key functions or even of the microbial community represents a well-directed tool for the identification of process problems and disturbances.Graphical abstractCorrelation between the different orders and process parameter, as well as principle component analysis of all investigated biogas plants based on the identified metaproteins. Electronic supplementary material The online version of this article (doi:10.1186/s13068-016-0572-4) contains supplementary material, which is available to authorized users.

Membrane bioreactors (MBR) are a relatively new and promising technology in the sector of advanced wastewater treatment (Yang et al., 2006). The combination of a bioreactor with a set of membrane modules enables the straightforward separation of treated sewage from activated sludge. MBRs compete by many advantages to conventional activated sludge processes (CAS), but its greatest feature leads to the support of both non-flocculating and flocculating bacteria. Therefore MBRs can be operated on higher loading rates and provide very high effluent quality. Nevertheless, MBRs still tend to membrane biofouling that impedes further process optimization. Biofouling is mainly caused by extracellular polymeric substances (EPS) and soluble microbial products (SMP) that accumulates onto and into the membrane. Consequently the permeate flux declines while simultaneously the pressure on the membrane (transmembrane pressure - TMP) inclines. Until recently, much research has been carried out to minimize biofouling effects. Engineering tools were mainly applied with regard to process optimization and monitoring. In contrast, biological tools to were rarely applied due to the fact that biofouling have been strongly related to the occurrence of EPS and SMP that consist mainly of polysaccharides and proteins. Thus, studies on DNA and/or RNA have become impractical in studying biofouling phenomena. Both engineering and biological tools did not reveal sufficient inside to the linkage between biofouling and biomass dynamics. There still exists a lack of knowledge about enzymatic activities and microbial dynamics of the MBR bioconsortium. However, a novel approach called "metaproteomics" has been recently proposed to explore environmental samples and activated sludge (Wilmes and Bond, 2004; Kan et al., 2005). This approach can deliver important information about the microbial enzymatic activity and can reflect reaction/adaptation of a biocommunity to its environment. It is for this the reason that metaproteomics will gain the understanding about biofouling in MBR. Here we present the first application of metaproteomic approach to MBR sludge, an extremely heterogeneous sample often found in environmental systems. We have developed a novel extraction and purification method based on phenol, especially for environmental samples (Benndorf et al., 2007). First studies were carried out on steady stage development of the MBR biomass and its specific reactions to a punctual salt shock load. It could be demonstrated that conventional engineering tools to monitor the reactor performance were not as sensitive as proteomic tools to reveal reactions and adaptations of the MBR biomass. Important protein-protein interactions could be detected by 2D-PAGE application only and provided a new inside into the complex nature of the MBR bioconsortium.