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

Reactive Transport: A Review of Basic Concepts with Emphasis on Biochemical Processes

Reactive transport (RT) couples bio-geo-chemical reactions and transport. RT is important to understand numerous scientific questions and solve some engineering problems. RT is highly multidisciplinary, which hinders the development of a body of knowledge shared by RT modelers and developers. The goal of this paper is to review the basic conceptual issues shared by all RT problems, so as to facilitate advancement along the current frontier: biochemical reactions. To this end, we review the basic equations to indicate that chemical systems are controlled by the set of equilibrium reactions, which are easy to model, but whose rate is controlled by mixing. Since mixing is not properly represented by the standard advection-dispersion equation (ADE), we conclude that this equation is poor for RT. This leads us to review alternative transport formulations, and the methods to solve RT problems using both the ADE and alternative equations. Since equilibrium is easy, difficulties arise for kinetic reactions, which is especially true for biochemistry, where numerous challenges are open (how to represent microbial communities, impact of genomics, effect of biofilms on flow and transport, etc.). We conclude with the basic eleven conceptual issues that we consider fundamental for any conceptually sound RT effort.

Genome-scale reconstruction of Paenarthrobacter aurescens TC1 metabolic model towards the study of atrazine bioremediation

Atrazine is an herbicide and a pollutant of great environmental concern that is naturally biodegraded by microbial communities. Paenarthrobacter aurescens TC1 is one of the most studied degraders of this herbicide. Here, we developed a genome scale metabolic model for P. aurescens TC1, iRZ1179, to study the atrazine degradation process at organism level. Constraint based flux balance analysis and time dependent simulations were used to explore the organism’s phenotypic landscape. Simulations aimed at designing media optimized for supporting growth and enhancing degradation, by passing the need in strain design via genetic modifications. Growth and degradation simulations were carried with more than 100 compounds consumed by P. aurescens TC1. In vitro validation confirmed the predicted classification of different compounds as efficient, moderate or poor stimulators of growth. Simulations successfully captured previous reports on the use of glucose and phosphate as bio-stimulators of atrazine degradation, supported by in vitro validation. Model predictions can go beyond supplementing the medium with a single compound and can predict the growth outcomes for higher complexity combinations. Hence, the analysis demonstrates that the exhaustive power of the genome scale metabolic reconstruction allows capturing complexities that are beyond common biochemical expertise and knowledge and further support the importance of computational platforms for the educated design of complex media. The model presented here can potentially serve as a predictive tool towards achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation.

Natural source zone depletion of LNAPL: A critical review supporting modelling approaches

Natural source zone depletion (NSZD) of light non-aqueous phase liquids (LNAPLs) includes partitioning, transport and degradation of LNAPL components. NSZD is being considered as a site closure option during later stages of active remediation of LNAPL contaminated sites, and where LNAPL mass removal is limiting. To ensure NSZD meets compliance criteria and to design enhanced NSZD actions if required, residual risks posed by LNAPL and its long term behaviour require estimation. Prediction of long-term NSZD trends requires linking physicochemical partitioning and transport processes with bioprocesses at multiple scales within a modelling framework. Here we expand and build on the knowledge base of a recent review of NSZD, to establish the key processes and understanding required to model NSZD long term. We describe key challenges to our understanding, inclusive of the dominance of methanogenic or aerobic biodegradation processes, the potentially changeability of rates due to the weathering profile of LNAPL product types and ages, and linkages to underlying bioprocesses. We critically discuss different scales in subsurface simulation and modelling of NSZD. Focusing on processes at Darcy scale, 36 models addressing processes of importance to NSZD are investigated. We investigate the capabilities of models to accommodate more than 20 subsurface transport and transformation phenomena and present comparisons in several tables. We discuss the applicability of each group of models for specific site conditions.

In Silico Metabolic Design of Two-Strain Biofilm Systems Predicts Enhanced Biomass Production and Biochemical Synthesis

Engineered biofilm consortia have the potential to solve important biotechnological problems that have proved difficult for monoculture biofilms and planktonic consortia, such as conversion of lignocellulosic material to useful biochemicals. While considerable experimental progress has been reported for engineering and characterizing biofilm consortia, the field still lacks in silico tools for simulation, design, and optimization of stable, robust, and productive designed consortia. We developed biofilm consortia metabolic models for two coculture systems centered around the ecological design motif of a primary cell type that utilizes a supplied electron donor and secretes acetate as a byproduct and a secondary cell type that consumes the acetate, relieving byproduct inhibition on the primary cell type and enhancing overall system biomass. The models presented in this paper predict that distinct metabolic niches for the two cell types could be established by supplying electron donors and acceptors at opposite ends of the biofilm and that acetate consumption by the secondary cell type could increase total biomass accumulation and the synthesis of valuable biochemicals, such as isobutanol, by the primary cell type. System tunability is enhanced when each cell type is supplied with a unique terminal electron acceptor at opposite ends of the biofilm rather than competing for a common electron acceptor. Our model provides good qualitative agreement with data for a synthetic Escherichia coli coculture system, suggesting that the proposed design rules may have wide applicability to engineered biofilm consortia.

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.

Spatiotemporal modeling of microbial metabolism

BACKGROUND

Microbial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are common, the development of spatiotemporal metabolic models has received little attention.

RESULTS

We present a general methodology for spatiotemporal metabolic modeling based on combining genome-scale reconstructions with fundamental transport equations that govern the relevant convective and/or diffusional processes in time and spatially varying environments. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We demonstrate our methodology by solving spatiotemporal metabolic models for two systems of considerable practical interest: (1) a bubble column reactor with the syngas fermenting bacterium Clostridium ljungdahlii; and (2) a chronic wound biofilm with the human pathogen Pseudomonas aeruginosa. Despite the complexity of the discretized models which consist of 900 ODEs/600 LPs and 250 ODEs/250 LPs, respectively, we show that the proposed computational framework allows efficient and robust model solution.

CONCLUSIONS

Our study establishes a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations and has wide applicability to natural and engineered microbial systems.

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.

Metabolic network modeling of microbial communities

Genome-scale metabolic network reconstructions and constraint-based analyses are powerful methods that have the potential to make functional predictions about microbial communities. Genome-scale metabolic networks are used to characterize the metabolic functions of microbial communities via several techniques including species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the 'enzyme-soup' approach, multiscale modeling, and others. There are many challenges in the field, including a need for tools that accurately assign high-level omics signals to individual community members, the need for improved automated network reconstruction methods, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be corresponding advances in the fields of ecology, health science, and microbial community engineering.

Metabolic modeling of synthesis gas fermentation in bubble column reactors

BACKGROUND

A promising route to renewable liquid fuels and chemicals is the fermentation of synthesis gas (syngas) streams to synthesize desired products such as ethanol and 2,3-butanediol. While commercial development of syngas fermentation technology is underway, an unmet need is the development of integrated metabolic and transport models for industrially relevant syngas bubble column reactors.

RESULTS

We developed and evaluated a spatiotemporal metabolic model for bubble column reactors with the syngas fermenting bacterium Clostridium ljungdahlii as the microbial catalyst. Our modeling approach involved combining a genome-scale reconstruction of C. ljungdahlii metabolism with multiphase transport equations that govern convective and dispersive processes within the spatially varying column. The reactor model was spatially discretized to yield a large set of ordinary differential equations (ODEs) in time with embedded linear programs (LPs) and solved using the MATLAB based code DFBAlab. Simulations were performed to analyze the effects of important process and cellular parameters on key measures of reactor performance including ethanol titer, ethanol-to-acetate ratio, and CO and H2 conversions.

CONCLUSIONS

Our computational study demonstrated that mathematical modeling provides a complementary tool to experimentation for understanding, predicting, and optimizing syngas fermentation reactors. These model predictions could guide future cellular and process engineering efforts aimed at alleviating bottlenecks to biochemical production in syngas bubble column reactors.

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.

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.

The design of long-term effective uranium bioremediation strategy using a community metabolic model

Acetate amendment at uranium contaminated sites in Rifle, CO. leads to an initial bloom of Geobacter accompanied by the removal of U(VI) from the groundwater, followed by an increase of sulfate-reducing bacteria (SRBs) which are poor reducers of U(VI). One of the challenges associated with bioremediation is the decay in Geobacter abundance, which has been attributed to the depletion of bio-accessible Fe(III), motivating the investigation of simultaneous amendments of acetate and Fe(III) as an alternative bioremediation strategy. In order to understand the community metabolism of Geobacter and SRBs during artificial substrate amendment, we have created a genome-scale dynamic community model of Geobacter and SRBs using the previously described Dynamic Multi-species Metabolic Modeling framework. Optimization techniques are used to determine the optimal acetate and Fe(III) addition profile. Field-scale simulation of acetate addition accurately predicted the in situ data. The simulations suggest that batch amendment of Fe(III) along with continuous acetate addition is insufficient to promote long-term bioremediation, while continuous amendment of Fe(III) along with continuous acetate addition is sufficient to promote long-term bioremediation. By computationally minimizing the acetate and Fe(III) addition rates as well as the difference between the predicted and target uranium concentration, we showed that it is possible to maintain the uranium concentration below the environmental safety standard while minimizing the cost of chemical additions. These simulations show that simultaneous addition of acetate and Fe(III) has the potential to be an effective uranium bioremediation strategy. They also show that computational modeling of microbial community is an important tool to design effective strategies for practical applications in environmental biotechnology.