Coupling a genome-scale metabolic model with a reactive transport model to describe in situ uranium bioremediation

Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching

Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial growth and metabolism. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated application of linear programming (LP) to obtain flux balance analysis (FBA) solutions in every time step and spatial grid. To address this challenge, we propose a new simulation method where we train and validate artificial neural networks (ANNs) using randomly sampled FBA solutions and incorporate the resulting surrogate FBA model (represented as algebraic equations) into RTMs as source/sink terms. We demonstrate the efficiency of our method via a case study of Shewanella oneidensis MR-1. During aerobic growth on lactate, S. oneidensis produces metabolic byproducts (such as pyruvate and acetate), which are subsequently consumed as alternative carbon sources when the preferred nutrients are depleted. To effectively simulate these complex dynamics, we used a cybernetic approach that models metabolic switches as the outcome of dynamic competition among multiple growth options. In both zero-dimensional batch and one-dimensional column configurations, the ANN-based surrogate models achieved substantial reduction of computational time by several orders of magnitude compared to the original LP-based FBA models. Moreover, the ANN models produced robust solutions without any special measures to prevent numerical instability. These developments significantly promote our ability to utilize genome-scale networks in complex, multi-physics, and multi-dimensional ecosystem modeling.

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Acinetobacter lwoffii

Acinetobacter lwoffii is widely considered to be a harmful bacterium that is resistant to medicines and disinfectants. A. lwoffii NL1 degrades phenols efficiently and shows promise as an aromatic compound degrader in antibiotic-contaminated environments. To gain a comprehensive understanding of A. lwoffii, the first genome-scale metabolic model of A. lwoffii was constructed using semi-automated and manual methods. The iNX811 model, which includes 811 genes, 1071 metabolites, and 1155 reactions, was validated using 39 unique carbon and nitrogen sources. Genes and metabolites critical for cell growth were analyzed, and 12 essential metabolites (mainly in the biosynthesis and metabolism of glycan, lysine, and cofactors) were identified as antibacterial drug targets. Moreover, to explore the metabolic response to phenols, metabolic flux was simulated by integrating transcriptomics, and the significantly changed metabolism mainly included central carbon metabolism, along with some transport reactions. In addition, the addition of substances that effectively improved phenol degradation was predicted and validated using the model. Overall, the reconstruction and analysis of model iNX811 helped to study the antimicrobial systems and biodegradation behavior of A. lwoffii.

A bidirectional kinetic reaction model to predict uranium distribution in permeable reactive bio-barrier with high-sulfate environment

The use of permeable reactive bio-barriers (Bio-PRBs) is a developing method for remediation of uranium groundwater pollution. However, some remediation effects are difficult to estimate when because of the subsurface environment. Advanced knowledge of uranium migration and reactions in Bio-PRBs is crucial for successful practical application. In this study, a bidirectional kinetic reaction model was developed for predicting uranium reduction in a Bio-PRB system. The research demonstrates that the model is able to predict the uranium migration and rapidly evaluate the Bio-PRBs performance. The results show that contact period and microbial growth are the key factors that affect the remediation performance of Bio-PRBs. Microbial growth could lead to a decrease in hydraulic conductivity (K). The hydraulic conductivity loss in the free microorganisms (FM) group was 0.8-2.3 m/d, which was significantly smaller than the immobilized microorganism (IM) group (0.8-5.2 m/d). Compared to the IM group, the simulation results reveal that longer contact reaction period improves the remediation performance of SO42- and uranium by 32.6% and 21.7%, respectively. The bidirectional reaction between microorganisms and pollutants leads to a decrease in the remediation efficiency. In addition, the model can be used to design standard Bio-PRBs for real field of uranium contanminated groundwater. To meet the remediation goal of groundwater, the width of IM group needs to be increased to 250 cm while 500 cm for FM group. Therefore, IM-PRBs save costs significantly. The model has successfully optimized Bio-PRBs and predicted uranium contaminant-plume evolution and microbial growth inhibition in different Bio-PRBs.

ORT: a workflow linking genome-scale metabolic models with reactive transport codes

MOTIVATION

Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As 'omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed.

RESULTS

We propose a workflow ('Omics to Reactive Transport-ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBase and PFLOTRAN.

AVAILABILITY AND IMPLEMENTATION

Interactive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Distinct methane-dependent biogeochemical states in Arctic seafloor gas hydrate mounds

Archaea mediating anaerobic methane oxidation are key in preventing methane produced in marine sediments from reaching the hydrosphere; however, a complete understanding of how microbial communities in natural settings respond to changes in the flux of methane remains largely uncharacterized. We investigate microbial communities in gas hydrate-bearing seafloor mounds at Storfjordrenna, offshore Svalbard in the high Arctic, where we identify distinct methane concentration profiles that include steady-state, recently-increasing subsurface diffusive flux, and active gas seepage. Populations of anaerobic methanotrophs and sulfate-reducing bacteria were highest at the seep site, while decreased community diversity was associated with a recent increase in methane influx. Despite high methane fluxes and methanotroph doubling times estimated at 5-9 months, microbial community responses were largely synchronous with the advancement of methane into shallower sediment horizons. Together, these provide a framework for interpreting subseafloor microbial responses to methane escape in a warming Arctic Ocean.

Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates.

Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

Microbial and Geochemical Dynamics of an Aquifer Stimulated for Microbial Induced Calcite Precipitation (MICP)

Microbially induced calcite precipitation (MICP) is an alternative to existing soil stabilization techniques for construction and erosion. As with any biologically induced process in soils or aquifers, it is important to track changes in the microbial communities that occur as a result of the treatment. Our research assessed how native microbial communities developed in response to injections of reactants (dilute molasses as a carbon source; urea as a source of nitrogen and alkalinity) that promoted MICP in a shallow aquifer. Microbial community composition (16S rRNA gene) and ureolytic potential (ureC gene copy numbers) were also measured in groundwater and artificial sediment. Aquifer geochemistry showed evidence of sulfate reduction, nitrification, denitrification, ureolysis, and iron reduction during the treatment. The observed changes in geochemistry corresponded to microbial community succession in the groundwater and this matched parallel geophysical and mineralogical evidence of calcite precipitation in the aquifer. We detected an increase in the number of ureC genes in the microbial communities at the end of the injection period, suggesting an increase in the abundance of microbes possessing this gene as needed to hydrolyze urea and stimulate MICP. We identify geochemical and biological markers that highlight the microbial community response that can be used along with geophysical and geotechnical evidence to assess progress of MICP.

Circadian clock helps cyanobacteria manage energy in coastal and high latitude ocean

The circadian clock coordinates cellular functions over the diel cycle in many organisms. The molecular mechanisms of the cyanobacterial clock are well characterized, but its ecological role remains a mystery. We present an agent-based model of Synechococcus (harboring a self-sustained, bona fide circadian clock) that explicitly represents genes (e.g., kaiABC), transcripts, proteins, and metabolites. The model is calibrated to data from laboratory experiments with wild type and no-clock mutant strains, and it successfully reproduces the main observed patterns of glycogen metabolism. Comparison of wild type and no-clock mutant strains suggests a main benefit of the clock is due to energy management. For example, it inhibits glycogen synthesis early in the day when it is not needed and energy is better used for making the photosynthesis apparatus. To explore the ecological role of the clock, we integrate the model into a dynamic, three-dimensional global circulation model that includes light variability due to seasonal and diel incident radiation and vertical extinction. Model output is compared with field data, including in situ gene transcript levels. We simulate cyanobaceria with and without a circadian clock, which allows us to quantify the fitness benefit of the clock. Interestingly, the benefit is weakest in the low latitude open ocean, where Prochlorococcus (lacking a self-sustained clock) dominates. However, our attempt to experimentally validate this testable prediction failed. Our study provides insights into the role of the clock and an example for how models can be used to integrate across multiple levels of biological organization.

The global soil community and its influence on biogeochemistry

Soil organisms represent the most biologically diverse community on land and govern the turnover of the largest organic matter pool in the terrestrial biosphere. The highly complex nature of these communities at local scales has traditionally obscured efforts to identify unifying patterns in global soil biodiversity and biogeochemistry. As a result, environmental covariates have generally been used as a proxy to represent the variation in soil community activity in global biogeochemical models. Yet over the past decade, broad-scale studies have begun to see past this local heterogeneity to identify unifying patterns in the biomass, diversity, and composition of certain soil groups across the globe. These unifying patterns provide new insights into the fundamental distribution and dynamics of organic matter on land.

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.

A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli

Over the last decades, predictive microbiology has made significant advances in the mathematical description of microbial spoiler and pathogen dynamics in or on food products. Recently, the focus of predictive microbiology has shifted from a (semi-)empirical population-level approach towards mechanistic models including information about the intracellular metabolism in order to increase model accuracy and genericness. However, incorporation of this subpopulation-level information increases model complexity and, consequently, the required run time to simulate microbial cell and population dynamics. In this paper, results of metabolic flux balance analyses (FBA) with a genome-scale model are used to calibrate a low-complexity linear model describing the microbial growth and metabolite secretion rates of Escherichia coli as a function of the nutrient and oxygen uptake rate. Hence, the required information about the cellular metabolism (i.e., biomass growth and secretion of cell products) is selected and included in the linear model without incorporating the complete intracellular reaction network. However, the applied FBAs are only representative for microbial dynamics under specific extracellular conditions, viz., a neutral medium without weak acids at a temperature of 37℃. Deviations from these reference conditions lead to metabolic shifts and adjustments of the cellular nutrient uptake or maintenance requirements. This metabolic dependency on extracellular conditions has been taken into account in our low-complex metabolic model. In this way, a novel approach is developed to take the synergistic effects of temperature, pH, and undissociated acids on the cell metabolism into account. Consequently, the developed model is deployable as a tool to describe, predict and control E. coli dynamics in and on food products under various combinations of environmental conditions. To emphasize this point,three specific scenarios are elaborated: (i) aerobic respiration without production of weak acid extracellular metabolites, (ii) anaerobic fermentation with secretion of mixed acid fermentation products into the food environment, and (iii) respiro-fermentative metabolic regimes in between the behaviors at aerobic and anaerobic conditions.

From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality

Models are important tools in microbial ecology. They can be used to advance understanding by helping to interpret observations and test hypotheses, and to predict the effects of ecosystem management actions or a different climate. Over the past decades, biological knowledge and ecosystem observations have advanced to the molecular and in particular gene level. However, microbial ecology models have changed less and a current challenge is to make them utilize the knowledge and observations at the genetic level. We review published models that explicitly consider genes and make predictions at the population or ecosystem level. The models can be grouped into three general approaches, i.e., metabolic flux, gene-centric and agent-based. We describe and contrast these approaches by applying them to a hypothetical ecosystem and discuss their strengths and weaknesses. An important distinguishing feature is how variation between individual cells (individuality) is handled. In microbial ecosystems, individual heterogeneity is generated by a number of mechanisms including stochastic interactions of molecules (e.g., gene expression), stochastic and deterministic cell division asymmetry, small-scale environmental heterogeneity, and differential transport in a heterogeneous environment. This heterogeneity can then be amplified and transferred to other cell properties by several mechanisms, including nutrient uptake, metabolism and growth, cell cycle asynchronicity and the effects of age and damage. For example, stochastic gene expression may lead to heterogeneity in nutrient uptake enzyme levels, which in turn results in heterogeneity in intracellular nutrient levels. Individuality can have important ecological consequences, including division of labor, bet hedging, aging and sub-optimality. Understanding the importance of individuality and the mechanism(s) underlying it for the specific microbial system and question investigated is essential for selecting the optimal modeling strategy.

Functional Enzyme-Based Approach for Linking Microbial Community Functions with Biogeochemical Process Kinetics

The kinetics of biogeochemical processes in natural and engineered environmental systems is typically described using Monod-type or modified Monod-type models. These models rely on biomass as surrogates for functional enzymes in microbial communities that catalyze biogeochemical reactions. A major challenge of applying such models is the difficulty of quantitatively measuring functional biomass for the constraining and validation of the models. However, omics-based approaches have been increasingly used to characterize microbial community structure, functions, and metabolites. Here, we propose an enzyme-based model that can incorporate omics data to link microbial community functions with biogeochemical process kinetics. The model treats enzymes as time-variable catalysts for biogeochemical reactions and applies a biogeochemical reaction network to incorporate intermediate metabolites. The sequences of genes and proteins from metagenomes, as well as those from the UniProt database, were used for targeted enzyme quantification and to provide insights into the dynamic linkage among functional genes, enzymes, and metabolites that are required in the model. The application of the model was demonstrated using denitrification, as an example, by comparing model simulations with measured functional enzymes, genes, denitrification substrates, and intermediates.

Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species

Background

Chronic wounds are often colonized by consortia comprised of different bacterial species growing as biofilms on a complex mixture of wound exudate. Bacteria growing in biofilms exhibit phenotypes distinct from planktonic growth, often rendering the application of antibacterial compounds ineffective. Computational modeling represents a complementary tool to experimentation for generating fundamental knowledge and developing more effective treatment strategies for chronic wound biofilm consortia.

Results

We developed spatiotemporal models to investigate the multispecies metabolism of a biofilm consortium comprised of two common chronic wound isolates: the aerobe Pseudomonas aeruginosa and the facultative anaerobe Staphylococcus aureus. By combining genome-scale metabolic reconstructions with partial differential equations for metabolite diffusion, the models were able to provide both temporal and spatial predictions with genome-scale resolution. The models were used to analyze the metabolic differences between single species and two species biofilms and to demonstrate the tendency of the two bacteria to spatially partition in the multispecies biofilm as observed experimentally. Nutrient gradients imposed by supplying glucose at the bottom and oxygen at the top of the biofilm induced spatial partitioning of the two species, with S. aureus most concentrated in the anaerobic region and P. aeruginosa present only in the aerobic region. The two species system was predicted to support a maximum biofilm thickness much greater than P. aeruginosa alone but slightly less than S. aureus alone, suggesting an antagonistic metabolic effect of P. aeruginosa on S. aureus. When each species was allowed to enhance its growth through consumption of secreted metabolic byproducts assuming identical uptake kinetics, the competitiveness of P. aeruginosa was further reduced due primarily to the more efficient lactate metabolism of S. aureus. Lysis of S. aureus by a small molecule inhibitor secreted from P. aeruginosa and/or P. aeruginosa aerotaxis were predicted to substantially increase P. aeruginosa competitiveness in the aerobic region, consistent with in vitro experimental studies.

Conclusions

Our biofilm modeling approach allows the prediction of individual species metabolism and interspecies interactions in both time and space with genome-scale resolution. This study yielded new insights into the multispecies metabolism of a chronic wound biofilm, in particular metabolic factors that may lead to spatial partitioning of the two bacterial species. We believe that P. aeruginosa lysis of S. aureus combined with nutrient competition is a particularly relevant scenario for which model predictions could be tested experimentally.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0334-8) contains supplementary material, which is available to authorized users.

Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can be used to analyze complex “omics” data and to infer and optimize metabolic processes. Thereby, SMN models are suitable to capitalize on advances in high-throughput molecular and metabolic data generation. SMN models are starting to be applied to describe microbial interactions during wastewater treatment, in-situ bioremediation, microalgae blooms methanogenic fermentation, and bioplastic production. Despite their current challenges, we envisage that SMN models have future potential for the design and development of novel growth media, biochemical pathways and synthetic microbial associations.

Genome-scale modelling of microbial metabolism with temporal and spatial resolution

Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.

Synthetic Ecology of Microbes: Mathematical Models and Applications

As the indispensable role of natural microbial communities in many aspects of life on Earth is uncovered, the bottom-up engineering of synthetic microbial consortia with novel functions is becoming an attractive alternative to engineering single-species systems. Here, we summarize recent work on synthetic microbial communities with a particular emphasis on open challenges and opportunities in environmental sustainability and human health. We next provide a critical overview of mathematical approaches, ranging from phenomenological to mechanistic, to decipher the principles that govern the function, dynamics and evolution of microbial ecosystems. Finally, we present our outlook on key aspects of microbial ecosystems and synthetic ecology that require further developments, including the need for more efficient computational algorithms, a better integration of empirical methods and model-driven analysis, the importance of improving gene function annotation, and the value of a standardized library of well-characterized organisms to be used as building blocks of synthetic communities.

Using Genome-scale Models to Predict Biological Capabilities

Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started.

Evidence of Geobacter-associated phage in a uranium-contaminated aquifer

Geobacter species may be important agents in the bioremediation of organic and metal contaminants in the subsurface, but as yet unknown factors limit the in situ growth of subsurface Geobacter well below rates predicted by analysis of gene expression or in silico metabolic modeling. Analysis of the genomes of five different Geobacter species recovered from contaminated subsurface sites indicated that each of the isolates had been infected with phage. Geobacter-associated phage sequences were also detected by metagenomic and proteomic analysis of samples from a uranium-contaminated aquifer undergoing in situ bioremediation, and phage particles were detected by microscopic analysis in groundwater collected from sediment enrichment cultures. Transcript abundance for genes from the Geobacter-associated phage structural proteins, tail tube Gp19 and baseplate J, increased in the groundwater in response to the growth of Geobacter species when acetate was added, and then declined as the number of Geobacter decreased. Western blot analysis of a Geobacter-associated tail tube protein Gp19 in the groundwater demonstrated that its abundance tracked with the abundance of Geobacter species. These results suggest that the enhanced growth of Geobacter species in the subsurface associated with in situ uranium bioremediation increased the abundance and activity of Geobacter-associated phage and show that future studies should focus on how these phages might be influencing the ecology of this site.

Metabolic modeling of spatial heterogeneity of biofilms in microbial fuel cells reveals substrate limitations in electrical current generation

Microbial fuel cells (MFCs) have been proposed as an alternative energy resource for the conversion of organic compounds to electricity. In an MFC, microorganisms such as Geobacter sulfurreducens form an anode-associated biofilm that can completely oxidize organic matter (electron donor) to carbon dioxide with direct electron transfer to the anode (electron acceptor). Mathematical models are useful in analyzing biofilm processes; however, existing models rely on Nernst-Monod type expressions, and evaluate extracellular processes separated from the intracellular metabolism of the microorganism. Thus, models that combine both extracellular and intracellular components, while addressing spatial heterogeneity, are essential for improved representation of biofilm processes. The goal of this work is to develop a model that integrates genome-scale metabolic models with the model of biofilm environment. This integrated model shows the variations of electrical current production and biofilm thickness under the presence/absence of NH4 in the bulk solution, and under varying maintenance energy demands. Further, sensitivity analysis suggested that conductivity is not limiting electrical current generation and that increasing cell density can lead to enhanced current generation. In addition, the modeling results also highlight instances such as the transformation into respiring cells, where the mechanism of electrical current generation during biofilm development is not yet clearly understood.

Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics

The interspecies exchange of metabolites plays a key role in the spatiotemporal dynamics of microbial communities. This raises the question of whether ecosystem-level behavior of structured communities can be predicted using genome-scale metabolic models for multiple organisms. We developed a modeling framework that integrates dynamic flux balance analysis with diffusion on a lattice and applied it to engineered communities. First, we predicted and experimentally confirmed the species ratio to which a two-species mutualistic consortium converges and the equilibrium composition of a newly engineered three-member community. We next identified a specific spatial arrangement of colonies, which gives rise to what we term the "eclipse dilemma": does a competitor placed between a colony and its cross-feeding partner benefit or hurt growth of the original colony? Our experimentally validated finding that the net outcome is beneficial highlights the complex nature of metabolic interactions in microbial communities while at the same time demonstrating their predictability.

Toward quantitative understanding on microbial community structure and functioning: a modeling-centered approach using degradation of marine oil spills as example

Molecular ecology approaches are rapidly advancing our insights into the microorganisms involved in the degradation of marine oil spills and their metabolic potentials. Yet, many questions remain open: how do oil-degrading microbial communities assemble in terms of functional diversity, species abundances and organization and what are the drivers? How do the functional properties of microorganisms scale to processes at the ecosystem level? How does mass flow among species, and which factors and species control and regulate fluxes, stability and other ecosystem functions? Can generic rules on oil-degradation be derived, and what drivers underlie these rules? How can we engineer oil-degrading microbial communities such that toxic polycyclic aromatic hydrocarbons are degraded faster? These types of questions apply to the field of microbial ecology in general. We outline how recent advances in single-species systems biology might be extended to help answer these questions. We argue that bottom-up mechanistic modeling allows deciphering the respective roles and interactions among microorganisms. In particular constraint-based, metagenome-derived community-scale flux balance analysis appears suited for this goal as it allows calculating degradation-related fluxes based on physiological constraints and growth strategies, without needing detailed kinetic information. We subsequently discuss what is required to make these approaches successful, and identify a need to better understand microbial physiology in order to advance microbial ecology. We advocate the development of databases containing microbial physiological data. Answering the posed questions is far from trivial. Oil-degrading communities are, however, an attractive setting to start testing systems biology-derived models and hypotheses as they are relatively simple in diversity and key activities, with several key players being isolated and a high availability of experimental data and approaches.

Gene-centric approach to integrating environmental genomics and biogeochemical models

Rapid advances in molecular microbial ecology have yielded an unprecedented amount of data about the evolutionary relationships and functional traits of microbial communities that regulate global geochemical cycles. Biogeochemical models, however, are trailing in the wake of the environmental genomics revolution, and such models rarely incorporate explicit representations of bacteria and archaea, nor are they compatible with nucleic acid or protein sequence data. Here, we present a functional gene-based framework for describing microbial communities in biogeochemical models by incorporating genomics data to provide predictions that are readily testable. To demonstrate the approach in practice, nitrogen cycling in the Arabian Sea oxygen minimum zone (OMZ) was modeled to examine key questions about cryptic sulfur cycling and dinitrogen production pathways in OMZs. Simulations support previous assertions that denitrification dominates over anammox in the central Arabian Sea, which has important implications for the loss of fixed nitrogen from the oceans. Furthermore, cryptic sulfur cycling was shown to attenuate the secondary nitrite maximum often observed in OMZs owing to changes in the composition of the chemolithoautotrophic community and dominant metabolic pathways. Results underscore the need to explicitly integrate microbes into biogeochemical models rather than just the metabolisms they mediate. By directly linking geochemical dynamics to the genetic composition of microbial communities, the method provides a framework for achieving mechanistic insights into patterns and biogeochemical consequences of marine microbes. Such an approach is critical for informing our understanding of the key role microbes play in modulating Earth's biogeochemistry.

Semi-automated curation of metabolic models via flux balance analysis: a case study with Mycoplasma gallisepticum

Primarily used for metabolic engineering and synthetic biology, genome-scale metabolic modeling shows tremendous potential as a tool for fundamental research and curation of metabolism. Through a novel integration of flux balance analysis and genetic algorithms, a strategy to curate metabolic networks and facilitate identification of metabolic pathways that may not be directly inferable solely from genome annotation was developed. Specifically, metabolites involved in unknown reactions can be determined, and potentially erroneous pathways can be identified. The procedure developed allows for new fundamental insight into metabolism, as well as acting as a semi-automated curation methodology for genome-scale metabolic modeling. To validate the methodology, a genome-scale metabolic model for the bacterium Mycoplasma gallisepticum was created. Several reactions not predicted by the genome annotation were postulated and validated via the literature. The model predicted an average growth rate of 0.358±0.12, closely matching the experimentally determined growth rate of M. gallisepticum of 0.244±0.03. This work presents a powerful algorithm for facilitating the identification and curation of previously known and new metabolic pathways, as well as presenting the first genome-scale reconstruction of M. gallisepticum.

Flux balance analysis (FBA) is a powerful approach for genome-scale metabolic modeling. It provides metabolic engineers with a tool for manipulating, predicting, and optimizing metabolism for biotechnological and biomedical purposes. However, we posit that it can also be used as tool for fundamental research in understanding and curating metabolic networks. Specifically, by using a genetic algorithm integrated with FBA, we developed a curation approach to identify missing reactions, incomplete reactions, and erroneous reactions. Additionally, it was possible to take advantage of the ensemble information from the genetic algorithm to identify the most critical reactions for curation. We tested our strategy using Mycoplasma gallisepticum as our model organism. Using the genome annotation as the basis, the preliminary genome-scale metabolic model consisted of 446 metabolites involved in 380 reactions. Carrying out our analysis, we found over 80 incorrect reactions and 16 missing reactions. Based upon the guidance of the algorithm, we were able to curate and resolve all discrepancies. The model predicted an average bacterial growth rate of 0.358±0.12 h⁻¹ compared to the experimentally observed 0.244±0.03 h⁻¹. Thus, our approach facilitated the curation of a genome-scale metabolic network and generated a high quality metabolic model.

Enrichment of specific protozoan populations during in situ bioremediation of uranium-contaminated groundwater

The importance of bacteria in the anaerobic bioremediation of groundwater polluted with organic and/or metal contaminants is well recognized and in some instances so well understood that modeling of the in situ metabolic activity of the relevant subsurface microorganisms in response to changes in subsurface geochemistry is feasible. However, a potentially significant factor influencing bacterial growth and activity in the subsurface that has not been adequately addressed is protozoan predation of the microorganisms responsible for bioremediation. In field experiments at a uranium-contaminated aquifer located in Rifle, CO, USA, acetate amendments initially promoted the growth of metal-reducing Geobacter species, followed by the growth of sulfate reducers, as observed previously. Analysis of 18S rRNA gene sequences revealed a broad diversity of sequences closely related to known bacteriovorous protozoa in the groundwater before the addition of acetate. The bloom of Geobacter species was accompanied by a specific enrichment of sequences most closely related to the ameboid flagellate, Breviata anathema, which at their peak accounted for over 80% of the sequences recovered. The abundance of Geobacter species declined following the rapid emergence of B. anathema. The subsequent growth of sulfate-reducing Peptococcaceae was accompanied by another specific enrichment of protozoa, but with sequences most similar to diplomonadid flagellates from the family Hexamitidae, which accounted for up to 100% of the sequences recovered during this phase of the bioremediation. These results suggest a prey-predator response with specific protozoa responding to increased availability of preferred prey bacteria. Thus, quantifying the influence of protozoan predation on the growth, activity and composition of the subsurface bacterial community is essential for predictive modeling of in situ uranium bioremediation strategies.

Molecular analysis of the in situ growth rates of subsurface Geobacter species

Molecular tools that can provide an estimate of the in situ growth rate of Geobacter species could improve understanding of dissimilatory metal reduction in a diversity of environments. Whole-genome microarray analyses of a subsurface isolate of Geobacter uraniireducens, grown under a variety of conditions, identified a number of genes that are differentially expressed at different specific growth rates. Expression of two genes encoding ribosomal proteins, rpsC and rplL, was further evaluated with quantitative reverse transcription-PCR (qRT-PCR) in cells with doubling times ranging from 6.56 h to 89.28 h. Transcript abundance of rpsC correlated best (r(2) = 0.90) with specific growth rates. Therefore, expression patterns of rpsC were used to estimate specific growth rates of Geobacter species during an in situ uranium bioremediation field experiment in which acetate was added to the groundwater to promote dissimilatory metal reduction. Initially, increased availability of acetate in the groundwater resulted in higher expression of Geobacter rpsC, and the increase in the number of Geobacter cells estimated with fluorescent in situ hybridization compared well with specific growth rates estimated from levels of in situ rpsC expression. However, in later phases, cell number increases were substantially lower than predicted from rpsC transcript abundance. This change coincided with a bloom of protozoa and increased attachment of Geobacter species to solid phases. These results suggest that monitoring rpsC expression may better reflect the actual rate that Geobacter species are metabolizing and growing during in situ uranium bioremediation than changes in cell abundance.

Evaluation of a genome-scale in silico metabolic model for Geobacter metallireducens by using proteomic data from a field biostimulation experiment

Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens-specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

A road map for the development of community systems (CoSy) biology

Microbial interactions are essential for all global geochemical cycles and have an important role in human health and disease. Although we possess general knowledge about the major processes within a microbial community, we are presently unable to decipher what role individual microorganisms have and how their individual actions influence others in the community. We also have limited knowledge with which to predict the effects of microbial interactions and community composition on the environment and vice versa. In this Opinion article, we describe how community systems (CoSy) biology will enable us to decode these complex relationships and will therefore improve our understanding of individual members of the community and the modes of interactions in which they engage.

Use of an uncertainty analysis for genome-scale models as a prediction tool for microbial growth processes in subsurface environments

The integration of genome-scale, constraint-based models of microbial cell function into simulations of contaminant transport and fate in complex groundwater systems is a promising approach to help characterize the metabolic activities of microorganisms in natural environments. In constraint-based modeling, the specific uptake flux rates of external metabolites are usually determined by Michaelis-Menten kinetic theory. However, extensive data sets based on experimentally measured values are not always available. In this study, a genome-scale model of Pseudomonas putida was used to study the key issue of uncertainty arising from the parametrization of the influx of two growth-limiting substrates: oxygen and toluene. The results showed that simulated growth rates are highly sensitive to substrate affinity constants and that uncertainties in specific substrate uptake rates have a significant influence on the variability of simulated microbial growth. Michaelis-Menten kinetic theory does not, therefore, seem to be appropriate for descriptions of substrate uptake processes in the genome-scale model of P. putida. Microbial growth rates of P. putida in subsurface environments can only be accurately predicted if the processes of complex substrate transport and microbial uptake regulation are sufficiently understood in natural environments and if data-driven uptake flux constraints can be applied.

Laboratory evolution of Geobacter sulfurreducens for enhanced growth on lactate via a single-base-pair substitution in a transcriptional regulator

The addition of organic compounds to groundwater in order to promote bioremediation may represent a new selective pressure on subsurface microorganisms. The ability of Geobacter sulfurreducens, which serves as a model for the Geobacter species that are important in various types of anaerobic groundwater bioremediation, to adapt for rapid metabolism of lactate, a common bioremediation amendment, was evaluated. Serial transfer of five parallel cultures in a medium with lactate as the sole electron donor yielded five strains that could metabolize lactate faster than the wild-type strain. Genome sequencing revealed that all five strains had non-synonymous single-nucleotide polymorphisms in the same gene, GSU0514, a putative transcriptional regulator. Introducing the single-base-pair mutation from one of the five strains into the wild-type strain conferred rapid growth on lactate. This strain and the five adaptively evolved strains had four to eight-fold higher transcript abundance than wild-type cells for genes for the two subunits of succinyl-CoA synthase, an enzyme required for growth on lactate. DNA-binding assays demonstrated that the protein encoded by GSU0514 bound to the putative promoter of the succinyl-CoA synthase operon. The binding sequence was not apparent elsewhere in the genome. These results demonstrate that a single-base-pair mutation in a transcriptional regulator can have a significant impact on the capacity for substrate utilization and suggest that adaptive evolution should be considered as a potential response of microorganisms to environmental change(s) imposed during bioremediation.

Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model

The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The modeling system is designed in such a way that constraint-based models targeting different microorganisms or competing organism communities can be easily plugged into the system. Constraint-based modeling is very costly given the size of a genome-scale reaction network. To save computation time, a binary tree is traversed to examine the concentration and solution pool generated during the simulation in order to decide whether the constraint-based model should be called. We also show preliminary results from the integrated model including a comparison of the direct and indirect coupling approaches and evaluated the ability of the approach to simulate field experiment.

Geobacter: the microbe electric's physiology, ecology, and practical applications

Geobacter species specialize in making electrical contacts with extracellular electron acceptors and other organisms. This permits Geobacter species to fill important niches in a diversity of anaerobic environments. Geobacter species appear to be the primary agents for coupling the oxidation of organic compounds to the reduction of insoluble Fe(III) and Mn(IV) oxides in many soils and sediments, a process of global biogeochemical significance. Some Geobacter species can anaerobically oxidize aromatic hydrocarbons and play an important role in aromatic hydrocarbon removal from contaminated aquifers. The ability of Geobacter species to reductively precipitate uranium and related contaminants has led to the development of bioremediation strategies for contaminated environments. Geobacter species produce higher current densities than any other known organism in microbial fuel cells and are common colonizers of electrodes harvesting electricity from organic wastes and aquatic sediments. Direct interspecies electron exchange between Geobacter species and syntrophic partners appears to be an important process in anaerobic wastewater digesters. Functional and comparative genomic studies have begun to reveal important aspects of Geobacter physiology and regulation, but much remains unexplored. Quantifying key gene transcripts and proteins of subsurface Geobacter communities has proven to be a powerful approach to diagnose the in situ physiological status of Geobacter species during groundwater bioremediation. The growth and activity of Geobacter species in the subsurface and their biogeochemical impact under different environmental conditions can be predicted with a systems biology approach in which genome-scale metabolic models are coupled with appropriate physical/chemical models. The proficiency of Geobacter species in transferring electrons to insoluble minerals, electrodes, and possibly other microorganisms can be attributed to their unique "microbial nanowires," pili that conduct electrons along their length with metallic-like conductivity. Surprisingly, the abundant c-type cytochromes of Geobacter species do not contribute to this long-range electron transport, but cytochromes are important for making the terminal electrical connections with Fe(III) oxides and electrodes and also function as capacitors, storing charge to permit continued respiration when extracellular electron acceptors are temporarily unavailable. The high conductivity of Geobacter pili and biofilms and the ability of biofilms to function as supercapacitors are novel properties that might contribute to the field of bioelectronics. The study of Geobacter species has revealed a remarkable number of microbial physiological properties that had not previously been described in any microorganism. Further investigation of these environmentally relevant and physiologically unique organisms is warranted.

Spatial aspects in biological system simulations

Mathematical models of the dynamical properties of biological systems aim to improve our understanding of the studied system with the ultimate goal of being able to predict system responses in the absence of experimentation. Despite the enormous advances that have been made in biological modeling and simulation, the inherently multiscale character of biological systems and the stochasticity of biological processes continue to present significant computational and conceptual challenges. Biological systems often consist of well-organized structural hierarchies, which inevitably lead to multiscale problems. This chapter introduces and discusses the advantages and shortcomings of several simulation methods that are being used by the scientific community to investigate the spatiotemporal properties of model biological systems. We first describe the foundations of the methods and then describe their relevance and possible application areas with illustrative examples from our own research. Possible ways to address the encountered computational difficulties are also discussed.

In situ to in silico and back: elucidating the physiology and ecology of Geobacter spp. using genome-scale modelling

There is a wide diversity of unexplored metabolism encoded in the genomes of microorganisms that have an important environmental role. Genome-scale metabolic modelling enables the individual reactions that are encoded in annotated genomes to be organized into a coherent whole, which can then be used to predict metabolic fluxes that will optimize cell function under a range of conditions. In this Review, we summarize a series of studies in which genome-scale metabolic modelling of Geobacter spp. has resulted in an in-depth understanding of their central metabolism and ecology. A similar iterative modelling and experimental approach could accelerate elucidation of the physiology and ecology of other microorganisms inhabiting a diversity of environments, and could guide optimization of the practical applications of these species.

The genome of Geobacter bemidjiensis, exemplar for the subsurface clade of Geobacter species that predominate in Fe(III)-reducing subsurface environments

Background

Geobacter species in a phylogenetic cluster known as subsurface clade 1 are often the predominant microorganisms in subsurface environments in which Fe(III) reduction is the primary electron-accepting process. Geobacter bemidjiensis, a member of this clade, was isolated from hydrocarbon-contaminated subsurface sediments in Bemidji, Minnesota, and is closely related to Geobacter species found to be abundant at other subsurface sites. This study examines whether there are significant differences in the metabolism and physiology of G. bemidjiensis compared to non-subsurface Geobacter species.

Results

Annotation of the genome sequence of G. bemidjiensis indicates several differences in metabolism compared to previously sequenced non-subsurface Geobacteraceae, which will be useful for in silico metabolic modeling of subsurface bioremediation processes involving Geobacter species. Pathways can now be predicted for the use of various carbon sources such as propionate by G. bemidjiensis. Additional metabolic capabilities such as carbon dioxide fixation and growth on glucose were predicted from the genome annotation. The presence of different dicarboxylic acid transporters and two oxaloacetate decarboxylases in G. bemidjiensis may explain its ability to grow by disproportionation of fumarate. Although benzoate is the only aromatic compound that G. bemidjiensis is known or predicted to utilize as an electron donor and carbon source, the genome suggests that this species may be able to detoxify other aromatic pollutants without degrading them. Furthermore, G. bemidjiensis is auxotrophic for 4-aminobenzoate, which makes it the first Geobacter species identified as having a vitamin requirement. Several features of the genome indicated that G. bemidjiensis has enhanced abilities to respire, detoxify and avoid oxygen.

Conclusion

Overall, the genome sequence of G. bemidjiensis offers surprising insights into the metabolism and physiology of Geobacteraceae in subsurface environments, compared to non-subsurface Geobacter species, such as the ability to disproportionate fumarate, more efficient oxidation of propionate, enhanced responses to oxygen stress, and dependence on the environment for a vitamin requirement. Therefore, an understanding of the activity of Geobacter species in the subsurface is more likely to benefit from studies of subsurface isolates such as G. bemidjiensis than from the non-subsurface model species studied so far.

Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments

The advent of rapid complete genome sequencing, and the potential to capture this information in genome-scale metabolic models, provide the possibility of comprehensively modeling microbial community interactions. For example, Rhodoferax and Geobacter species are acetate-oxidizing Fe(III)-reducers that compete in anoxic subsurface environments and this competition may have an influence on the in situ bioremediation of uranium-contaminated groundwater. Therefore, genome-scale models of Geobacter sulfurreducens and Rhodoferax ferrireducens were used to evaluate how Geobacter and Rhodoferax species might compete under diverse conditions found in a uranium-contaminated aquifer in Rifle, CO. The model predicted that at the low rates of acetate flux expected under natural conditions at the site, Rhodoferax will outcompete Geobacter as long as sufficient ammonium is available. The model also predicted that when high concentrations of acetate are added during in situ bioremediation, Geobacter species would predominate, consistent with field-scale observations. This can be attributed to the higher expected growth yields of Rhodoferax and the ability of Geobacter to fix nitrogen. The modeling predicted relative proportions of Geobacter and Rhodoferax in geochemically distinct zones of the Rifle site that were comparable to those that were previously documented with molecular techniques. The model also predicted that under nitrogen fixation, higher carbon and electron fluxes would be diverted toward respiration rather than biomass formation in Geobacter, providing a potential explanation for enhanced in situ U(VI) reduction in low-ammonium zones. These results show that genome-scale modeling can be a useful tool for predicting microbial interactions in subsurface environments and shows promise for designing bioremediation strategies.

Systems approaches to microbial communities and their functioning

Recent advances in molecular microbial ecology and systems biology enhance insight into microbial community structure and functioning. They provide conceptual and technical bases for the translation of species-data and community-data into a model framework accounting for the functioning of and interactions between metabolic networks of species in multispecies environments. Function-directed and single cell-directed approaches supplement and improve metagenomics-derived community information. The topology of the metabolic network, reconstructed from a species' genome sequence, provides insight into its metabolic environments and interactions with other microorganisms. Progress in the theoretical and experimental analysis of flux through metabolic networks paves the way for their application at the community level, contributing to understanding of material flows between and within species and their resilience toward perturbations.

Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling

Background

Geobacter sulfurreducens is a member of the Geobacter species, which are capable of oxidation of organic waste coupled to the reduction of heavy metals and electrode with applications in bioremediation and bioenergy generation. While the metabolism of this organism has been studied through the development of a stoichiometry based genome-scale metabolic model, the associated regulatory network has not yet been well studied. In this manuscript, we report on the implementation of a thermodynamics based metabolic flux model for Geobacter sulfurreducens. We use this updated model to identify reactions that are subject to regulatory control in the metabolic network of G. sulfurreducens using thermodynamic variability analysis.

Findings

As a first step, we have validated the regulatory sites and bottleneck reactions predicted by the thermodynamic flux analysis in E. coli by evaluating the expression ranges of the corresponding genes. We then identified ten reactions in the metabolic network of G. sulfurreducens that are predicted to be candidates for regulation. We then compared the free energy ranges for these reactions with the corresponding gene expression fold changes under conditions of different environmental and genetic perturbations and show that the model predictions of regulation are consistent with data. In addition, we also identify reactions that operate close to equilibrium and show that the experimentally determined exchange coefficient (a measure of reversibility) is significant for these reactions.

Conclusions

Application of the thermodynamic constraints resulted in identification of potential bottleneck reactions not only from the central metabolism but also from the nucleotide and amino acid subsystems, thereby showing the highly coupled nature of the thermodynamic constraints. In addition, thermodynamic variability analysis serves as a valuable tool in estimating the ranges of Δ_rG' of every reaction in the model leading to the prediction of regulatory sites in the metabolic network, thereby characterizing the regulatory network in both a model organism such as E. coli as well as a non model organism such as G. sulfurreducens.

Modeling and sensitivity analysis of electron capacitance for Geobacter in sedimentary environments

In situ stimulation of the metabolic activity of Geobacter species through acetate amendment has been shown to be a promising bioremediation strategy to reduce and immobilize hexavalent uranium [U(VI)] as insoluble U(IV). Although Geobacter species are reducing U(VI), they primarily grow via Fe(III) reduction. Unfortunately, the biogeochemistry and the physiology of simultaneous reduction of multiple metals are still poorly understood. A detailed model is therefore required to better understand the pathways leading to U(VI) and Fe(III) reduction by Geobacter species. Based on recent experimental evidence of temporary electron capacitors in Geobacter we propose a novel kinetic model that physically distinguishes planktonic cells into electron-loaded and -unloaded states. Incorporation of an electron load-unload cycle into the model provides insight into U(VI) reduction efficiency, and elucidates the relationship between U(VI)- and Fe(III)-reducing activity and further explains the correlation of high U(VI) removal with high fractions of planktonic cells in subsurface environments. Global sensitivity analysis was used to determine the level of importance of geochemical and microbial processes controlling Geobacter growth and U(VI) reduction, suggesting that the electron load-unload cycle and the resulting repartition of the microbes between aqueous and attached phases are critical for U(VI) reduction. As compared with conventional Monod modeling approaches without inclusion of the electron capacitance, the new model attempts to incorporate a novel cellular mechanism that has a significant impact on the outcome of in situ bioremediation.