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

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

Metabolic Modeling and Engineering of Gas Fermentation in Bubble Column Reactors

Gas fermentation is an attractive route to produce alternative fuels and chemicals from non-food feedstocks, such as waste gas streams from steel mills and synthesis gas (mainly CO and H2) produced from municipal solid waste through gasification. While commercial development of gas fermentation technology is underway, many research problems must be addressed to further advance the technology towards economic competitiveness. A particularly important challenge is to develop integrated metabolic and transport models that describe gas fermentation in industrially relevant bubble column reactors. I have developed and evaluated a spatiotemporal metabolic model for bubble column reactors with the syngas fermenting bacterium Clostridium ljungdahlii as the microbial catalyst. My 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). I used the MATLAB based code DFBAlab to efficiently and robustly solve the discretized model, which consisted of 900 ODEs and 600 LPs due to the use of lexicographic optimization. Column startup was dynamically simulated under different operating conditions. The resulting steady-state solutions were compared to analyze the effect of operating parameters on key measures of reactor performance including ethanol titer, ethanol-to-acetate ratio, and CO and H2 conversions. I showed that the bubble column configuration outperformed a traditional stirred tank reactor in terms of ethanol productivity when computationally evaluated at comparable operating conditions. In addition to providing new insights into bottlenecks to biochemical production in syngas bubble column reactors, the study established a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations. I also performed in silico metabolic engineering studies using the genome-scale reconstruction of C. ljungdahlii metabolism and the OptKnock computational framework to identify gene knockouts that were predicted to enhance the synthesis of these native and non-native products, introduced through insertion of the necessary heterologous pathways. The OptKnock derived strategies were often difficult to assess because increase product synthesis was invariably accompanied by decreased growth. Therefore, the OptKnock strategies were further evaluated using my spatiotemporal metabolic model of syngas fermentation. Unlike conventional flux balance analysis, the bubble column model accounted for the complex tradeoffs between increased product synthesis and reduced growth rates of engineered mutants within the spatially varying column environment. The two-stage methodology for deriving and evaluating metabolic engineering strategies was shown to yield new C. ljungdahlii gene targets that offer the potential for increased product synthesis under realistic syngas fermentation conditions. Clostridium autoethanogenum, an acetogenic bacterium, was developed by LanzaTech and shows high potential in production of ethanol and 2,3-butanediol from industry waste gas (mainly CO and CO2) via fermentation. I developed a spatiotemporal metabolic model using steady-state CO fermentation data collected from a laboratory-scale bubble column reactor at LanzaTech. The bubble column model provided good agreement with measured ethanol, acetate and biomass concentrations obtained at a single gas flow rate. To obtain satisfactory steady-state predictions over a range of gas flow rates, the upper bound of the proton exchange flux in the C. autoethanogenum genome-scale reconstruction was correlated with the gas flow rate as an indirect means to account for the effects of acetate secretion on extracellular pH. These results demonstrate that the modeling method established in this thesis have strong potential to facilitate commercial-scale design of gas fermentation processes for production of biofuel and biochemicals

Genome-driven evolutionary game theory helps understand the rise of metabolic interdependencies in microbial communities

Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial “games”. We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.We gratefully acknowledge funding from the Defense Advanced Research Projects Agency (Purchase Request No. HR0011515303, Contract No. HR0011-15-C-0091), the U.S. Department of Energy (Grants DE-SC0004962 and DE-SC0012627), the NIH (Grants 5R01DE024468 and R01GM121950), the national Science Foundation (Grants 1457695 and NSFOCE-BSF 1635070), MURI Grant W911NF-12-1-0390, the Human Frontiers Science Program (grant RGP0020/2016), and the Boston University Interdisciplinary Biomedical Research Office ARC grant on Systems Biology Approaches to Microbiome Research. We also thank Dr Kirill Korolev and members of the Segre Lab for their invaluable feedback on this work. (HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; DE-SC0004962 - U.S. Department of Energy; DE-SC0012627 - U.S. Department of Energy; 5R01DE024468 - NIH; R01GM121950 - NIH; 1457695 - national Science Foundation; NSFOCE-BSF 1635070 - national Science Foundation; W911NF-12-1-0390 - MURI; RGP0020/2016 - Human Frontiers Science Program; Boston University Interdisciplinary Biomedical Research Office ARC)Published versio

Metabolic Modeling of Bacterial Co-cultures for CO-to-Butyrate Conversion in Bubble Column Bioreactors

One of the most promising routes to renewable liquid fuels and chemicals is the fermentation of waste carbon by specialized microbes. Commercial development of gas fermentation technology is underway but many fundamental research problems must be addressed to further advance the technology towards economic competitiveness. This thesis addresses the important problem of developing integrated metabolic and transport models that predict gas fermentation performance in industrially relevant bubble column reactors. The computational models describe the conversion of CO-rich waste streams including synthesis gas to the platform chemical butyrate. The proposed modeling approach involves combining genome-scale reconstructions of bacterial species metabolism with transport equations that govern the relevant multiphase convective and diffusional processes within the spatially-varying system. I compared the combination of the acetogen Clostridium autoethanogenum for CO conversion to the intermediate acetate and three different gut bacteria (Clostridium hylemonae, Eubacterium rectale and Roseburia hominis) for conversion of acetate to butyrate. Trial-and-error optimization of the three co-culture designs was performed to assess their relative performance and guide future experimental studies

Spatiotemporal Metabolic Network Models Reveal Complex Autotroph-Heterotroph Biofilm Interactions Governed by Photon Incidences

Autotroph-heterotroph interactions are ubiquitous in natural environment and play a key role in controlling various essential ecosystem functions, such as production and utilization of organic matter, cycling of nitrogen, sulfur, and other chemical elements. Understanding how these biofilm metabolic interactions are constrained in space and time remains challenging because fully predictive models designed for this purpose are currently limited. Toward filling this gap, here we developed community metabolic network models for two autotroph-heterotroph biofilm consortia (termed UCC-A and UCC-O), which share a suite of common heterotrophic members but have a single distinct photoautotrophic cyanobacterium (Phormidesmis priestleyi str. ANA and Phormidium sp. OSCR) that provides organic carbon and nitrogen sources to support the growth of heterotrophic partners. After determining model parameters by data fitting using the spatiotemporal distributions of microbial abundances, we comparatively analyzed the resulting biofilm models to examine any fundamental differences in microbial interactions between the two consortia under the variation of key environmental variables: CO2 and photon levels. The UCC-A model predicted generally expected responses, i.e., the autotroph population increased in response to elevated levels of CO2 and photon, followed by increase in the heterotroph population. In contrast, the UCC-O model showed somewhat complicated dynamics, e.g., higher photon incidence rates resulted in the increase in autotroph population but decrease in heterotroph population due to the lowered provision of glucose from the autotroph. A further analysis showed that species coexistence was governed by the photon incidences rather than the carbon availability for UCC-O, which was the opposite for UCC-A

Spatiotemporal Metabolic Network Models Reveal Complex Autotroph-Heterotroph Biofilm Interactions Governed by Photon Incidences

Autotroph-heterotroph interactions are ubiquitous in natural environment and play a key role in controlling various essential ecosystem functions, such as production and utilization of organic matter, cycling of nitrogen, sulfur, and other chemical elements. Understanding how these biofilm metabolic interactions are constrained in space and time remains challenging because fully predictive models designed for this purpose are currently limited. Toward filling this gap, here we developed community metabolic network models for two autotroph-heterotroph biofilm consortia (termed UCC-A and UCC-O), which share a suite of common heterotrophic members but have a single distinct photoautotrophic cyanobacterium (Phormidesmis priestleyi str. ANA and Phormidium sp. OSCR) that provides organic carbon and nitrogen sources to support the growth of heterotrophic partners. After determining model parameters by data fitting using the spatiotemporal distributions of microbial abundances, we comparatively analyzed the resulting biofilm models to examine any fundamental differences in microbial interactions between the two consortia under the variation of key environmental variables: CO2 and photon levels. The UCC-A model predicted generally expected responses, i.e., the autotroph population increased in response to elevated levels of CO2 and photon, followed by increase in the heterotroph population. In contrast, the UCC-O model showed somewhat complicated dynamics, e.g., higher photon incidence rates resulted in the increase in autotroph population but decrease in heterotroph population due to the lowered provision of glucose from the autotroph. A further analysis showed that species coexistence was governed by the photon incidences rather than the carbon availability for UCC-O, which was the opposite for UCC-A

METABOLIC MODELING OF MULTISPECIES MICROBIAL BIOFILMS

Biofilms are ubiquitous in medical, environmental, and engineered microbial systems. The majority of naturally occurring microbes grow as mixed species biofilms. These complicated biofilm consortia are comprised of many cell phenotypes with complex interactions and self-organized into three-dimensional structures. Approximately 2% of the US population suffers from non-healing chronic wounds infected by a combination of commensal and pathogenic bacteria whereas about 500,000 cases of Clostridium difficile infections (CDI) are reported annually. These polymicrobial infections are often resilient to antibiotic treatment due to the nutrient-rich environments and species interactions that promote community stability and robustness. This thesis focusses on developing metabolic modeling framework to study the interactions and the spatial/temporal organizations in the biofilms. The modeling framework is based on integrating genome scale metabolic reconstructions of considered species in this work, with the nutrient uptake kinetics to predict the species abundances, growth rates and byproduct secretions. The spatiotemporal modeling framework accounts for the nutrient concentration gradients in the biofilm system. Spatiotemporal biofilm metabolic models were formulated by combining genome scale metabolic reconstructions of considered species with uptake kinetics for available nutrients and reaction-diffusion type equations for species biomass, supplied substrates and synthesized metabolic byproducts. The resulting partial differential equations embedded with linear programs were discretized in the space and integrated using a dynamic flux balance method. This framework was used to calculate the spatial and temporal variations in the species, nutrient and byproduct concentrations in biofilms. This framework was used to study the species organization and dynamics in chronic wound infections, CDI and environmental biofilms. The chronic wound biofilm model was comprising of two most dominant species, Pseudomonas aeruginosa and Staphylococcus aureus. The CDI biofilm model was comprising of representative species from three most common phyla in gut Bacteroidetes thetaiotaomicron, Faecalibacterium prausnitzii, Escherichia coli and pathogen C. difficile. The simulation results were used to study the interspecies interactions, the spatial partitioning in the biofilms and important crossfeeding relationships within the community. These predictions would be useful in devising effective antibiotic treatment strategies to cure the biofilm infections associated with chronic wounds and C. difficile. The environmental biofilm model for cyanobacteria and heterotrophs was developed and validated with the experimental results, this model was used to evaluate the community dynamics under extreme environmental conditions The second modeling framework considered biofilm as a well-mixed homogenous system at steady state. Steady state in silico community models were formulated by combining genome scale metabolic reconstructions of the considered species. The community models were solved using SteadyCom method. This method uses community flux balance analysis to calculate the relative abundance of each species with an objective of maximizing the community growth rate. A 12 species chronic wound community metabolic model covering 74% of 16S rDNA pyrosequencing reads of dominant genera from 2,963 chronic wound patients was developed. The community model was used to predict species abundances averaged across this large patient population. The simulation results from this study were used to identify putative mutualistic interactions between bacteria that could be targeted to enhance treatment efficacy. The frameworks developed in this thesis would be useful in developing patient/disease specific therapeutic treatments

Metabolic Modeling of Gas Fermentation for Renewable Fuel and Chemical Production

Gas fermentation has emerged as a technologically and economically attractive option for producing renewable fuels and chemicals from carbon monoxide (CO) rich waste streams. As compared to traditional catalyst technologies, microbial systems have several advantages including operation near ambient temperature and pressure, high conversion efficiencies, robustness to gas impurities and high product yields that have motivated both fundamental research and commercial development. While microbial production of high-value products from waste gases is challenging because wild-type strains capable of gas consumption tend to synthesize these products at low yields, strategy like metabolically engineering the gas fermenting acetogens have been studied to address this issue. Meanwhile, another promising alternative is to take advantage of the native capabilities for producing high-value products of wild-type strains and use coculture designs by combining gas-fermenting acetogens with bacterial strains that offer high yields of desired product. In this study, motivated by our industrial collaborator LanzaTech, we first focused on combining hydrodynamics with a genome-scale reconstruction of Clostridium autoethanogenum metabolism and multiphase convection-dispersion equations to compare the performance of bubble column reactors with and without liquid recycle. For both reactor configurations, hydrodynamics was predicted to diminish bubble column performance when compared to bubble column models in which the gas phase was modeled as ideal plug flow plus axial dispersion. Liquid recycle was predicted to be advantageous by increasing CO conversion, biomass production, and ethanol and 2,3-butanediol production compared to the non-recycle reactor configuration. After this, we explored the possibilities of producing an important platform chemical butyrate by using wild-type strains in the continuous stirred tank bioreactors and developed two anaerobic coculture designs by combining C. autoethanogenum for CO-to-acetate conversion with environmental bacterium Clostridium kluyveri and the human gut bacterium Eubacterium rectale which offer high acetate-to-butyrate conversion. A bubble column model developed to assess the potential for large-scale butyrate production of the C. autoethanogenum-E. rectale design predicted that a 40/30/30 CO/H2/N2 gas mixture and a 5 meter column length would be preferred to enhance C. autoethanogenum growth and counteract CO inhibitory effects on E. rectale. This research was further developed by exploiting the diversity of 4 acetogens and 818 human gut bacteria for anaerobic synthesis of butyrate from acetate and ethanol. A total of 170 acetogen/gut bacterium/sugar combinations were dynamically simulated for continuous growth using a 70/30 CO/CO2 feed gas mixture and minimal media computationally determined for each combination. Our models generated several coculture designs with promising performance and robustness. Furthermore, our models indicate a general methodology for determining coculture designs in silico and expanding the product range for gas fermenting. We believe that our study represents an important contribution towards the development of microbial platforms for gas fermentation