Thermodynamics and H2 Transfer in a Methanogenic, Syntrophic Community

Precise metabolic modeling in post-omics era: accomplishments and perspectives

Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.

An integrated systems biology approach reveals differences in formate metabolism in the genus Methanothermobacter

Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus Methanothermobacter have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three Methanothermobacter strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates. Using an integrated systems biology approach, we identified differences in formate anabolism between the strains and revealed that formate anabolism influences the diversion of carbon between biomass and methane. This finding, together with the omics datasets and the metabolic models we generated, can be implemented for biotechnological applications of Methanothermobacter in power-to-gas technology, and as a perspective, for value-added chemical production.

Modelling of microbial interactions in anaerobic digestion: from black to glass box

Anaerobic and microaerophilic environments are pervasive in nature, providing essential contributions to the maintenance of human health, biogeochemical cycles and the Earth's climate. These ecological niches are characterised by low free oxygen and oxidants, or lack thereof. Under these conditions, interactions between species are essential for supporting the growth of syntrophic species and maintaining thermodynamic feasibility of anaerobic fermentation. Kinetic models provide a simplified view of complex metabolic networks, while genome-scale metabolic models and flux-balance analysis (FBA) aim to unravel these systems as a whole. The target of this review is to outline the main similarities, differences and challenges associated with kinetic and metabolic modelling, and describe state-of-the-art modelling practices for studying syntrophies in the anaerobic digestion (AD) case study.

Iron-modified biochar boosts anaerobic digestion of sulfamethoxazole pharmaceutical wastewater: Performance and microbial mechanism

The accumulation of volatile fatty acids (VFAs) caused by antibiotic inhibition significantly reduces the treatment efficiency of sulfamethoxazole (SMX) wastewater. Few studies have been conducted to study the VFAs gradient metabolism of extracellular respiratory bacteria (ERB) and hydrogenotrophic methanogen (HM) under high-concentration sulfonamide antibiotics (SAs). And the effects of iron-modified biochar on antibiotics are unknown. Here, the iron-modified biochar was added to an anaerobic baffled reactor (ABR) to intensify the anaerobic digestion of SMX pharmaceutical wastewater. The results demonstrated that ERB and HM were developed after adding iron-modified biochar, promoting the degradation of butyric, propionic and acetic acids. The content of VFAs reduced from 1166.0 mg L^-1 to 291.5 mg L^-1. Therefore, chemical oxygen demand (COD) and SMX removal efficiency were improved by 22.76% and 36.51%, and methane production was enhanced by 6.19 times. Furthermore, the antibiotic resistance genes (ARGs) such as sul1, sul2, intl1 in effluent were decreased by 39.31%, 43.33%, 44.11%. AUTHM297 (18.07%), Methanobacterium (16.05%), Geobacter (6.05%) were enriched after enhancement. The net energy after enhancement was 0.7122 kWh m^-3. These results confirmed that ERB and HM were enriched via iron-modified biochar to achieve high efficiency of SMX wastewater treatment.

Stimulating Anaerobic Degradation of Butyrate via Syntrophomonas wolfei and Geobacter sulfurreducens: Characteristics and Mechanism

Anaerobic digestion (AD) has been widely applied for the degradation of organic wastewater due to its advantages of high-load operation and energy recovery. However, some challenges, such as low treatment capacity and instability caused by the accumulation of volatile fatty acids, limit its further application. Here, S. wolfei and G. sulfurreducens were initially co-cultured in the anaerobic anode of bio-electrochemical system for degrading butyric acid. Butyrate degradation characteristics in different conditions were quantitatively described. Moreover, G. sulfurreducens simultaneously strengthened the consumption of H₂ and acetic acid via direct interspecies electron transfer, thereby strengthening the degradation of butyric acid via a co-metabolic process. During butyrate degradation, the co-culture of S. wolfei and G. sulfurreducens showed more advantages than that of S. wolfei and methanogens. This present study provides a new perspective of butyrate metabolism, which was independent of methanogens in an AD process.

Prospects for multi-omics in the microbial ecology of water engineering

Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions - including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, related developments in both whole community gene expression surveys and metabolite profiling have permitted for direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Water engineers, microbiologists, and microbial ecologists studying activated sludge, anaerobic digestion, and drinking water distribution systems have applied various (meta)omics techniques for connecting microbial community dynamics and physiologies to overall process parameters and system performance. However, the rapid pace at which new omics-based approaches are developed can appear daunting to those looking to apply these state-of-the-art practices for the first time. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.

Novel insights into the anaerobic digestion of propionate via Syntrophobacter fumaroxidans and Geobacter sulfurreducens: Process and mechanism

The accumulation of volatile fatty acids, particularly propionic acid, significantly inhibits the efficiency of the anaerobic digestion system. In propionate degradation metabolism, the unfavorable thermodynamics of syntrophic reactions, strict ecological niche of syntrophic priopionate oxidizing bacteria, and slow metabolic rate of methanogens are regarded as major limitations. In this study, Geobacter sulfurreducens was co-cultured with Syntrophobacter fumaroxidans in bioelelectrochemical cells to analyze the propionate degradation process, impact factor, mechanism metabolic pathways, and electron transfer comprehensively. The results revealed that the syntroph S. fumaroxidans and syntrophic partner G. sulfurreducens achieved more efficient propionate degradation than the control group, comprising S. fumaroxidans and methanogens. Moreover, the carbon resource concentration and pH were both significantly correlated with propionate degradation (P < 0.01). The results further confirmed that G. sulfurreducen strengthened the consumption of H₂ and acetate via direct interspecific electron transfer in propionate degradation. These findings indicate that G. sulfurreducens plays an unidentified functional role in propionate degradation.

Designing microbial communities to maximize the thermodynamic driving force for the production of chemicals

Microbial communities have become a major research focus due to their importance for biogeochemical cycles, biomedicine and biotechnological applications. While some biotechnological applications, such as anaerobic digestion, make use of naturally arising microbial communities, the rational design of microbial consortia for bio-based production processes has recently gained much interest. One class of synthetic microbial consortia is based on specifically designed strains of one species. A common design principle for these consortia is based on division of labor, where the entire production pathway is divided between the different strains to reduce the metabolic burden caused by product synthesis. We first show that classical division of labor does not automatically reduce the metabolic burden when metabolic flux per biomass is analyzed. We then present ASTHERISC (Algorithmic Search of THERmodynamic advantages in Single-species Communities), a new computational approach for designing multi-strain communities of a single-species with the aim to divide a production pathway between different strains such that the thermodynamic driving force for product synthesis is maximized. ASTHERISC exploits the fact that compartmentalization of segments of a product pathway in different strains can circumvent thermodynamic bottlenecks arising when operation of one reaction requires a metabolite with high and operation of another reaction the same metabolite with low concentration. We implemented the ASTHERISC algorithm in a dedicated program package and applied it on E. coli core and genome-scale models with different settings, for example, regarding number of strains or demanded product yield. These calculations showed that, for each scenario, many target metabolites (products) exist where a multi-strain community can provide a thermodynamic advantage compared to a single strain solution. In some cases, a production with sufficiently high yield is thermodynamically only feasible with a community. In summary, the developed ASTHERISC approach provides a promising new principle for designing microbial communities for the bio-based production of chemicals.

Communities of microbes are ubiquitous in nature and also of high relevance for industrial applications, e.g. for the production of biogas. The development and use of non-natural communities for biotechnological applications has become an important subject of research. In this work, we present a new computational method to design synthetic communities with improved capabilities for the synthesis of desired target metabolites. Our method takes a constraint-based metabolic model of an organism as input and searches for a suitable partitioning of the product pathway via different strains of the organism such that the thermodynamic driving force for product synthesis is maximized. Essentially, this approach exploits the fact that having multiple strains allows adjustment of different metabolite concentrations in the different strains by which the thermodynamic driving force for product synthesis can often be increased. We tested this approach with a core and with a genome-scale metabolic network model of Escherichia coli. We found that, for dozens of metabolites, there exist communities with specifically designed strains of E. coli where the maximal thermodynamic driving force can be increased compared to a single E. coli strain. In summary, our presented method provides a new approach, together with a new design principle, for the computational design of microbial communities.

Comprehensive Bioenergetic Evaluation of Microbial Pathway Variants in Syntrophic Propionate Oxidation

In this work, an original methodology was developed that quantifies bioenergetically and physiologically feasible net ATP yields for large numbers of microbial metabolic pathways and their variants under different conditions. All variants are evaluated, which ensures global optimality in finding the pathway variant(s) leading to the highest ATP yield.

In this work, a systematic methodology was developed (based on known biochemistry, physiology, and bioenergetics) for the automated feasibility evaluation and net ATP yield quantification of large sets of pathway variants. Possible pathway variants differ in their intermediate metabolites, in which electron carriers are involved, in which steps are consuming/producing ATP, and in which steps are coupled to (and to how many) proton (or its equivalent) translocations. A pathway variant is deemed feasible, under a given set of physiological and environmental conditions, only if all pathway reaction steps have nonpositive Gibbs energy changes and if all the metabolite concentrations remain within an acceptable physiological range (10⁻⁶ to 10⁻² M). The complete understanding of syntrophic propionate oxidation remains elusive due to uncertainties in pathways and the mechanisms for interspecies electron transfer (IET). Several million combinations of pathway variants and parameters/conditions were evaluated for propionate oxidation, providing unprecedented mechanistic insight into its biochemical and bioenergetic landscape. Our results show that, under a scenario of optimum environmental conditions for propionate oxidation, the Smithella pathway yields the most ATP and the methylmalonyl-coenzyme A (CoA) pathways can generate sufficient ATP for growth only under a cyclical pathway configuration with pyruvate. The results under conditions typical of methanogenic environments show that propionate oxidation via the lactate and via the hydroxypropionyl-CoA pathways yield the most ATP. IET between propionate oxidizers and methanogens can proceed either by dissolved hydrogen via the Smithella pathway or by different mechanisms (e.g., formate or direct IET) if other pathways are used.

IMPORTANCE In this work, an original methodology was developed that quantifies bioenergetically and physiologically feasible net ATP yields for large numbers of microbial metabolic pathways and their variants under different conditions. All variants are evaluated, which ensures global optimality in finding the pathway variant(s) leading to the highest ATP yield. The methodology is designed to be especially relevant to hypothesize on which microbial pathway variants should be most favored in microbial ecosystems under high selective pressure for efficient metabolic energy conservation. Syntrophic microbial oxidation of propionate to acetate has an extremely small quantity of available energy and requires an extremely high metabolic efficiency to sustain life. Our results bring mechanistic insights into the optimum pathway variants, other metabolic bottlenecks, and the impact of environmental conditions on the ATP yields. Additionally, our results conclude that, as previously reported, under specific conditions, IET mechanisms other than hydrogen must exist to simultaneously sustain the growth of both propionate oxidizers and hydrogenotrophic methanogens.

Low-Temperature Pretreatment of Organic Feedstocks with Selected Mineral Wastes Sustains Anaerobic Digestion Stability through Trace Metal Release

A low-cost approach for enhancing mesophilic (37 °C) anaerobic digestion (AD) of organic waste using a low-temperature (37 °C) pretreatment with different mineral wastes (MW) was investigated. A higher and stable methane production rate, in comparison to MW-free controls, was achieved for 80 days at organic loading rates of 1-2 g VS/L·d, using a feed substrate pretreated with incinerator bottom ash (IBA). The boiler ash and cement-based waste pretreatments also produced high methane production rates but with some process instability. In contrast, an incinerator fly ash pretreatment showed a progressive decrease in methane production rates and poor process stability, leading to reactor failure after 40 days. To avoid process instability and/or reactor failure, two metrics had to be met: (a) a methanogenesis to fermentation ratio higher than 0.6 and (b) a cell-specific methanogenic activity to cell-specific fermentation activity ratio of >1000. The prevalence of Methanofastidiosum together with a mixed community of acetoclastic (Methanosaeta) and hydrogenotrophic (Methanobacterium) methanogens in the stable IBA treatment indicated the importance of Methanofastidiosum as a potential indicator of a healthy and stable reactor.

μBialSim: Constraint-Based Dynamic Simulation of Complex Microbiomes

Microbial communities are pervasive in the natural environment, associated with many hosts, and of increasing importance in biotechnological applications. The complexity of these microbial systems makes the underlying mechanisms driving their dynamics difficult to identify. While experimental meta-OMICS techniques are routinely applied to record the inventory and activity of microbiomes over time, it remains difficult to obtain quantitative predictions based on such data. Mechanistic, quantitative mathematical modeling approaches hold the promise to both provide predictive power and shed light on cause-effect relationships driving these dynamic systems. We introduce μbialSim (pronounced "microbial sim"), a dynamic Flux-Balance-Analysis-based (dFBA) numerical simulator which is able to predict the time course in terms of composition and activity of microbiomes containing 100s of species in batch or chemostat mode. Activity of individual species is simulated by using separate FBA models which have access to a common pool of compounds, allowing for metabolite exchange. A novel augmented forward Euler method ensures numerical accuracy by temporarily reducing the time step size when compound concentrations decrease rapidly due to high compound affinities and/or the presence of many consuming species. We present three exemplary applications of μbialSim: a batch culture of a hydrogenotrophic archaeon, a syntrophic methanogenic biculture, and a 773-species human gut microbiome which exhibits a complex and dynamic pattern of metabolite exchange. Focusing on metabolite exchange as the main interaction type, μbialSim allows for the mechanistic simulation of microbiomes at their natural complexity. Simulated trajectories can be used to contextualize experimental meta-OMICS data and to derive hypotheses on cause-effect relationships driving community dynamics based on scenario simulations. μbialSim is implemented in Matlab and relies on the COBRA Toolbox or CellNetAnalyzer for FBA calculations. The source code is available under the GNU General Public License v3.0 at https://git.ufz.de/UMBSysBio/microbialsim.

Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity

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.

Co-digestion of organic and mineral wastes for enhanced biogas production: Reactor performance and evolution of microbial community and function

Mineral wastes (MWs) from municipal solid waste incineration plants and construction demolition sites are rich in minerals, heavy metals and have acid neutralising capacity. This renders such MWs a promising source of bulk and trace elements to enhance and stabilize biogas production in anaerobic processes. However, finding a MW with typical heavy metal concentrations, which promotes anaerobic digestion (AD) without adverse effects on the microbial community of the reactor is of major importance. To investigate the impact of several MW additives (1. incineration bottom ash; 2. fly ash; 3. boiler ash; 4. cement-based waste) as AD co-substrates, six 5 L single stage mesophilic, continuously stirred tank reactors (CSTR) were setup. Two different feeding regimes were employed including: (a) a liquid-recycled feeding method (LRFM); (b) a draw-and-fill feeding method (DFFM). Under the LRFM regime, one gram MW per gram organic waste enhanced process stability (pH), increased methane production (25-45% increase), and yielded (450-520 mL CH₄/g VS); DFFM enhanced digestibility to a lesser degree. Illumina HiSeq 16S rRNA community sequencing of reactors showed that the microbial community compositions were unaffected by the presence of MW additives in comparison to unamended controls, but MW amendment accelerated bacterial growth (determined by qPCR). In contrast, different feeding regimes altered the microbial communities; Methanoculleus (hydrogenotrophic) and Methanosaeta (acetoclastic) were the most abundant methanogenic genera in the LRFM reactors, and the more metabolically versatile Methanosarcina genus dominated under DFFM.

Determination of Microbial Maintenance in Acetogenesis and Methanogenesis by Experimental and Modeling Techniques

For biogas-producing continuous stirred tank reactors, an increase in dilution rate increases the methane production rate as long as substrate input can be converted fully. However, higher dilution rates necessitate higher specific microbial growth rates, which are assumed to have a strong impact on the apparent microbial biomass yield due to cellular maintenance. To test this, we operated two reactors at 37°C in parallel at dilution rates of 0.18 and 0.07 days-1 (hydraulic retention times of 5.5 and 14 days, doubling times of 3.9 and 9.9 days in steady state) with identical inoculum and a mixture of volatile fatty acids as sole carbon sources. We evaluated the performance of the Anaerobic Digestion Model No. 1 (ADM1), a thermodynamic black box approach (TBA), and dynamic flux balance analysis (dFBA), to describe the experimental observations. All models overestimated the impact of dilution rate on the apparent microbial biomass yield when using default parameter values. Based on our analysis, a maintenance coefficient value below 0.2 kJ per carbon mole of microbial biomass per hour should be used for the TBA, corresponding to 0.12 mmol ATP per gram dry weight per hour for dFBA, which strongly deviates from the value of 9.8 kJ Cmol h-1 that has been suggested to apply to all anaerobic microorganisms at 37°C. We hypothesized that a decrease in dilution rate might select taxa with minimized maintenance expenditure. However, no major differences in the dominating taxa between the reactors were observed based on amplicon sequencing of 16S rRNA genes and terminal restriction fragment length polymorphism analysis of mcrA genes. Surprisingly, Methanosaeta dominated over Methanosarcina even at a dilution rate of 0.18 days-1, which contradicts previous model expectations. Furthermore, only 23-49% of the bacterial reads could be assigned to known syntrophic fatty acid oxidizers, indicating that unknown members of this functional group remain to be discovered. In conclusion, microbial maintenance was found to be much lower for acetogenesis and methanogenesis than previously assumed, likely due to the exceptionally low growth rates in anaerobic digestion. This finding might also be relevant for other microbial systems operating at similarly low growth rates.

RedCom: A strategy for reduced metabolic modeling of complex microbial communities and its application for analyzing experimental datasets from anaerobic digestion

Constraint-based modeling (CBM) is increasingly used to analyze the metabolism of complex microbial communities involved in ecology, biomedicine, and various biotechnological processes. While CBM is an established framework for studying the metabolism of single species with linear stoichiometric models, CBM of communities with balanced growth is more complicated, not only due to the larger size of the multi-species metabolic network but also because of the bilinear nature of the resulting community models. Moreover, the solution space of these community models often contains biologically unrealistic solutions, which, even with model linearization and under application of certain objective functions, cannot easily be excluded. Here we present RedCom, a new approach to build reduced community models in which the metabolisms of the participating organisms are represented by net conversions computed from the respective single-species networks. By discarding (single-species) net conversions that violate a minimality criterion in the exchange fluxes, it is ensured that unrealistic solutions in the community model are excluded where a species altruistically synthesizes large amounts of byproducts (instead of biomass) to fulfill the requirements of other species. We employed the RedCom approach for modeling communities of up to nine organisms involved in typical degradation steps of anaerobic digestion in biogas plants. Compared to full (bilinear and linearized) community models, we found that the reduced community models obtained with RedCom are not only much smaller but allow, also in the largest model with nine species, extensive calculations required to fully characterize the solution space and to reveal key properties of communities with maximum methane yield and production rates. Furthermore, the predictive power of the reduced community models is significantly larger because they predict much smaller ranges of feasible community compositions and exchange fluxes still being consistent with measurements obtained from enrichment cultures. For an enrichment culture for growth on ethanol, we also used metaproteomic data to further constrain the solution space of the community models. Both model and proteomic data indicated a dominance of acetoclastic methanogens (Methanosarcinales) and Desulfovibrionales being the least abundant group in this microbial community.

Microbial communities are involved in many fundamental processes in nature, health and biotechnology. The elucidation of interdependencies between the involved players of microbial communities and how the interactions shape the composition, behavior and characteristic features of the consortium has become an important branch of microbiology research. Many communities are based on the exchange of metabolites between the species and constraint-based metabolic modeling has become an important approach for a formal description and quantitative analysis of these metabolic dependencies. However, the complexity of the models rises quickly with a growing number of organisms and the space of predicted feasible behaviors often includes unrealistic solutions. Here we present RedCom, a new approach to build reduced stoichiometric models of balanced microbial communities based on net conversions of the single-species models. We demonstrate the applicability of our RedCom approach by modeling communities of up to nine organisms involved in degradation steps of anaerobic digestion in biogas plants. As one of the first studies in this field, we compare simulation results from the community models with experimental data of laboratory-scale biogas reactors for growth on ethanol and glucose-cellulose media. The results also demonstrate a higher predictive power of the RedCom vs. the full models.

Understanding and Engineering Distributed Biochemical Pathways in Microbial Communities

Microbiomes impact nearly every environment on Earth by modulating the molecular composition of the environment. Temporally changing environmental stimuli and spatial organization are major variables shaping the structure and function of microbiomes. The web of interactions among members of these communities and between the organisms and the environment dictates microbiome functions. Microbial interactions are major drivers of microbiomes and are modulated by spatiotemporal parameters. A mechanistic and quantitative understanding of ecological, molecular, and environmental forces shaping microbiomes could inform strategies to control microbiome dynamics and functions. Major challenges for harnessing the potential of microbiomes for diverse applications include the development of predictive modeling frameworks and tools for precise manipulation of microbiome behaviors.

Metabolic Modeling of Microbial Community Interactions for Health, Environmental and Biotechnological Applications

In nature, microbes do not exist in isolation but co-exist in a variety of ecological and biological environments and on various host organisms. Due to their close proximity, these microbes interact among themselves, and also with the hosts in both positive and negative manners. Moreover, these interactions may modulate dynamically upon external stimulus as well as internal community changes. This demands systematic techniques such as mathematical modeling to understand the intrinsic community behavior. Here, we reviewed various approaches for metabolic modeling of microbial communities. If detailed species-specific information is available, segregated models of individual organisms can be constructed and connected via metabolite exchanges; otherwise, the community may be represented as a lumped network of metabolic reactions. The constructed models can then be simulated to help fill knowledge gaps, and generate testable hypotheses for designing new experiments. More importantly, such community models have been developed to study microbial interactions in various niches such as host microbiome, biogeochemical and bioremediation, waste water treatment and synthetic consortia. As such, the metabolic modeling efforts have allowed us to gain new insights into the natural and synthetic microbial communities, and design interventions to achieve specific goals. Finally, potential directions for future development in metabolic modeling of microbial communities were also discussed.

RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

Elucidation of complexity and prediction of interactions in microbial communities

Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ. Interpretation of these multi-omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.

BiKEGG: a COBRA toolbox extension for bridging the BiGG and KEGG databases

Development of an interface tool between the Biochemical, Genetic and Genomic (BiGG) and KEGG databases is necessary for simultaneous access to the features of both databases. For this purpose, we present the BiKEGG toolbox, an open source COBRA toolbox extension providing a set of functions to infer the reaction correspondences between the KEGG reaction identifiers and those in the BiGG knowledgebase using a combination of manual verification and computational methods. Inferred reaction correspondences using this approach are supported by evidence from the literature, which provides a higher number of reconciled reactions between these two databases compared to the MetaNetX and MetRxn databases. This set of equivalent reactions is then used to automatically superimpose the predicted fluxes using COBRA methods on classical KEGG pathway maps or to create a customized metabolic map based on the KEGG global metabolic pathway, and to find the corresponding reactions in BiGG based on the genome annotation of an organism in the KEGG database. Customized metabolic maps can be created for a set of pathways of interest, for the whole KEGG global map or exclusively for all pathways for which there exists at least one flux carrying reaction. This flexibility in visualization enables BiKEGG to indicate reaction directionality as well as to visualize the reaction fluxes for different static or dynamic conditions in an animated manner. BiKEGG allows the user to export (1) the output visualized metabolic maps to various standard image formats or save them as a video or animated GIF file, and (2) the equivalent reactions for an organism as an Excel spreadsheet.

Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function

Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism's biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria.