50 results
[50]M. R. Long, J. L. Reed, "Improving Flux Predictions by Integrating Data from Multiple Strains.", Bioinformatics (Oxford, England), dec 2016, pp. btw706. [abstract] [doi]
ABSTRACT: MOTIVATION Incorporating experimental data into constraint-based models can improve the quality and accuracy of their metabolic flux predictions. Unfortunately, routinely and easily measured experimental data such as growth rates, extracellular fluxes, transcriptomics, and even proteomics are not always sufficient to significantly improve metabolic flux predictions. RESULTS We developed a new method (called REPPS) for incorporating experimental measurements of growth rates and extracellular fluxes from a set of perturbed reference strains (RSs) and a parental strain (PS) to substantially improve the predicted flux distribution of the parental strain. Using data from five single gene knockouts and the wild type strain, we decrease the mean squared error of predicted central metabolic fluxes by \~47% compared to parsimonious flux balance analysis (pFBA). This decrease in error further improves flux predictions for new knockout strains. Furthermore, REPPS is less sensitive to the completeness of the metabolic network than pFBA. CONTACT reed@engr.wisc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
[49]X. Zhang, C. J. Tervo and J. L. Reed, "Metabolic Assessment of E. coli as a Biofactory for Commercial Products", Metabolic Engineering, vol. 35, feb 2016, pp. 64–74. [abstract] [doi]
ABSTRACT: Metabolic engineering uses microorganisms to synthesize chemicals from renewable resources. Given the thousands of known metabolites, it is unclear what valuable chemicals could be produced by a microorganism and what native and heterologous reactions are needed for their synthesis. To answer these questions, a systematic computational assessment of Escherichia coli’s potential ability to produce different chemicals was performed using an integrated metabolic model that included native E.coli reactions and known heterologous reactions. By adding heterologous reactions, a total of 1777 non-native products could theoretically be produced in E. coli under glucose minimal medium conditions, of which 279 non-native products have commercial applications. Synthesis pathways involving native and heterologous reactions were identified from eight central metabolic precursors to the 279 non-native commercial products. These pathways were used to evaluate the dependence on, and diversity of, native and heterologous reactions to produce each non-native commercial product, as well as to identify each product׳s closest central metabolic precursor. Analysis of the synthesis pathways (with 5 or fewer reaction steps) to non-native commercial products revealed that isopentenyl diphosphate, pyruvate, and oxaloacetate are the closest central metabolic precursors to the most non-native commercial products. Additionally, 4-hydroxybenzoate, tyrosine, and phenylalanine were found to be common precursors to a large number of non-native commercial products. Strains capable of producing high levels of these precursors could be further engineered to create strains capable of producing a variety of commercial non-native chemicals.
[48]C. J. Tervo, J. L. Reed, "MapMaker and PathTracer for tracking carbon in genome-scale metabolic models.", Biotechnology journal, 2016. [abstract] [doi]
ABSTRACT: Constraint-based reconstruction and analysis (COBRA) modeling results can be difficult to interpret given the large numbers of reactions in genome-scale models. While paths in metabolic networks can be found, existing methods are not easily combined with constraint-based approaches. To address this limitation, two tools (MapMaker and PathTracer) were developed to find paths (including cycles) between metabolites, where each step transfers carbon from reactant to product. MapMaker predicts carbon transfer maps (CTMs) between metabolites using only information on molecular formulae and reaction stoichiometry, effectively determining which reactants and products share carbon atoms. MapMaker correctly assigned CTMs for over 97% of the 2,251 reactions in an Escherichia coli metabolic model (iJO1366). Using CTMs as inputs, PathTracer finds paths between two metabolites. PathTracer was applied to iJO1366 to investigate the importance of using CTMs and COBRA constraints when enumerating paths, to find active and high flux paths in flux balance analysis (FBA) solutions, to identify paths for putrescine utilization, and to elucidate a potential CO2 fixation pathway in E. coli. These results illustrate how MapMaker and PathTracer can be used in combination with constraint-based models to identify feasible, active, and high flux paths between metabolites.
[47]C. Cotten, J. L. Reed, "Applications of Constraint-Based Models for Biochemical Production", in Biotechnology for Biofuel Production and Optimization, 1 ed. C. Eckert, C. T. Trinh, Eds., Elsevier, 2016, pp. 201–226.
[46]M. R. Long, W. K. Ong and J. L. Reed, "Computational methods in metabolic engineering for strain design", Current Opinion in Biotechnology, vol. 34, aug 2015, pp. 135–141. [abstract] [doi]
ABSTRACT: Metabolic engineering uses genetic approaches to control microbial metabolism to produce desired compounds. Computational tools can identify new biological routes to chemicals and the changes needed in host metabolism to improve chemical production. Recent computational efforts have focused on exploring what compounds can be made biologically using native, heterologous, and/or enzymes with broad specificity. Additionally, computational methods have been developed to suggest different types of genetic modifications (e.g. gene deletion/addition or up/down regulation), as well as suggest strategies meeting different criteria (e.g. high yield, high productivity, or substrate co-utilization). Strategies to improve the runtime performances have also been developed, which allow for more complex metabolic engineering strategies to be identified. Future incorporation of kinetic considerations will further improve strain design algorithms.
[45]J. J. Hamilton, M. Calixto Contreras and J. L. Reed, "Thermodynamics and H2 Transfer in a Methanogenic, Syntrophic Community", PLOS Computational Biology, vol. 11, no. 7, jul 2015, pp. e1004364. [abstract] [doi]
ABSTRACT: Microorganisms in nature do not exist in isolation but rather interact with other species in their environment. Some microbes interact via syntrophic associations, in which the metabolic by-products of one species serve as nutrients for another. These associations sustain a variety of natural communities, including those involved in methanogenesis. In anaerobic syntrophic communities, energy is transferred from one species to another, either through direct contact and exchange of electrons, or through small molecule diffusion. Thermodynamics plays an important role in governing these interactions, as the oxidation reactions carried out by the first community member are only possible because degradation products are consumed by the second community member. This work presents the development and analysis of genome-scale network reconstructions of the bacterium Syntrophobacter fumaroxidans and the methanogenic archaeon Methanospirillum hungatei. The models were used to verify proposed mechanisms of ATP production within each species. We then identified additional constraints and the cellular objective function required to match experimental observations. The thermodynamic S. fumaroxidans model could not explain why S. fumaroxidans does not produce H2 in monoculture, indicating that current methods might not adequately estimate the thermodynamics, or that other cellular processes (e.g., regulation) play a role. We also developed a thermodynamic coculture model of the association between the organisms. The coculture model correctly predicted the exchange of both H2 and formate between the two species and suggested conditions under which H2 and formate produced by S. fumaroxidans would be fully consumed by M. hungatei.
[44]C. J. Tervo, J. L. Reed, "Expanding metabolic engineering algorithms using feasible space and shadow price constraint modules", Metabolic Engineering Communications, vol. 1, dec 2014, pp. 1–11. [abstract] [doi]
ABSTRACT: While numerous computational methods have been developed that use genome-scale models to propose mutants for the purpose of metabolic engineering, they generally compare mutants based on a single criteria (e.g., production rate at a mutant's maximum growth rate). As such, these approaches remain limited in their ability to include multiple complex engineering constraints. To address this shortcoming, we have developed feasible space and shadow price constraint (FaceCon and ShadowCon) modules that can be added to existing mixed integer linear adaptive evolution metabolic engineering algorithms, such as OptKnock and OptORF. These modules allow strain designs to be identified amongst a set of multiple metabolic engineering algorithm solutions that are capable of high chemical production while also satisfying additional design criteria. We describe the various module implementations and their potential applications to the field of metabolic engineering. We then incorporated these modules into the OptORF metabolic engineering algorithm. Using an Escherichia coli genome-scale model (iJO1366), we generated different strain designs for the anaerobic production of ethanol from glucose, thus demonstrating the tractability and potential utility of these modules in metabolic engineering algorithms.
[43]X. Zhang, J. L. Reed, "Adaptive Evolution of Synthetic Cooperating Communities Improves Growth Performance", PLoS ONE, vol. 9, no. 10, jan 2014, pp. e108297. [abstract] [doi]
ABSTRACT: Symbiotic interactions between organisms are important for human health and biotechnological applications. Microbial mutualism is a widespread phenomenon and is important in maintaining natural microbial communities. Although cooperative interactions are prevalent in nature, little is known about the processes that allow their initial establishment, govern population dynamics and affect evolutionary processes. To investigate cooperative interactions between bacteria, we constructed, characterized, and adaptively evolved a synthetic community comprised of leucine and lysine Escherichia coli auxotrophs. The co-culture can grow in glucose minimal medium only if the two auxotrophs exchange essential metabolites - lysine and leucine (or its precursors). Our experiments showed that a viable co-culture using these two auxotrophs could be established and adaptively evolved to increase growth rates (by ∼3 fold) and optical densities. While independently evolved co-cultures achieved similar improvements in growth, they took different evolutionary trajectories leading to different community compositions. Experiments with individual isolates from these evolved co-cultures showed that changes in both the leucine and lysine auxotrophs improved growth of the co-culture. Interestingly, while evolved isolates increased growth of co-cultures, they exhibited decreased growth in mono-culture (in the presence of leucine or lysine). A genome-scale metabolic model of the co-culture was also constructed and used to investigate the effects of amino acid (leucine or lysine) release and uptake rates on growth and composition of the co-culture. When the metabolic model was constrained by the estimated leucine and lysine release rates, the model predictions agreed well with experimental growth rates and composition measurements. While this study and others have focused on cooperative interactions amongst community members, the adaptive evolution of communities with other types of interactions (e.g., commensalism, ammensalism or parasitism) would also be of interest.
[42]W. K. Ong, T. T. Vu, K. N. Lovendahl, J. M. Llull, M. H. Serres, M. F. Romine, J. L. Reed, "Comparisons of Shewanella strains based on genome annotations, modeling, and experiments", BMC Systems Biology, vol. 8, no. 1, jan 2014, pp. 31. [abstract] [doi]
ABSTRACT: Shewanella is a genus of facultatively anaerobic, Gram-negative bacteria that have highly adaptable metabolism which allows them to thrive in diverse environments. This quality makes them an attractive bacterial target for research in bioremediation and microbial fuel cell applications. Constraint-based modeling is a useful tool for helping researchers gain insights into the metabolic capabilities of these bacteria. However, Shewanella oneidensis MR-1 is the only strain with a genome-scale metabolic model constructed out of 21 sequenced Shewanella strains.$\backslash$nPMID: 24621294
[41]J. J. Hamilton, J. L. Reed, "Software platforms to facilitate reconstructing genome-scale metabolic networks", Environmental Microbiology, vol. 16, no. 1, 2014, pp. 49–59. [abstract] [doi]
ABSTRACT: System-level analyses of microbial metabolism are facilitated by genome-scale reconstructions of microbial biochemical networks. A reconstruction provides a structured representation of the biochemical transformations occurring within an organism, as well as the genes necessary to carry out these transformations, as determined by the annotated genome sequence and experimental data. Network reconstructions also serve as platforms for constraint-based computational techniques, which facilitate biological studies in a variety of applications, including evaluation of network properties, metabolic engineering and drug discovery. Bottom-up metabolic network reconstructions have been developed for dozens of organisms, but until recently, the pace of reconstruction has failed to keep up with advances in genome sequencing. To address this problem, a number of software platforms have been developed to automate parts of the reconstruction process, thereby alleviating much of the manual effort previously required. Here, we review four such platforms in the context of established guidelines for network reconstruction. While many steps of the reconstruction process have been successfully automated, some manual evaluation of the results is still required to ensure a high-quality reconstruction. Widespread adoption of these platforms by the scientific community is underway and will be further enabled by exchangeable formats across platforms.
[40]T. T. Vu, E. a. Hill, L. a. Kucek, A. E. Konopka, A. S. Beliaev, J. L. Reed, "Computational evaluation of Synechococcus sp. PCC 7002 metabolism for chemical production", Biotechnology Journal, vol. 8, no. 5, 2013, pp. 619–630. [abstract] [doi]
ABSTRACT: Cyanobacteria are ideal metabolic engineering platforms for carbon-neutral biotechnology because they directly convert CO2 to a range of valuable products. In this study, we present a computational assessment of biochemical production in Synechococcus sp. PCC 7002 (Synechococcus 7002), a fast growing cyanobacterium whose genome has been sequenced, and for which genetic modification methods have been developed. We evaluated the maximum theoretical yields (mol product per mol CO2 or mol photon) of producing various chemicals under photoautotrophic and dark conditions using a genome-scale metabolic model of Synechococcus 7002. We found that the yields were lower under dark conditions, compared to photoautotrophic conditions, due to the limited amount of energy and reductant generated from glycogen. We also examined the effects of photon and CO2 limitations on chemical production under photoautotrophic conditions. In addition, using various computational methods such as minimization of metabolic adjustment (MOMA), relative metabolic change (RELATCH), and OptORF, we identified gene-knockout mutants that are predicted to improve chemical production under photoautotrophic and/or dark anoxic conditions. These computational results are useful for metabolic engineering of cyanobacteria to synthesize value-added products.
[39]C. J. Tervo, J. L. Reed, "BioMog: A computational framework for the de novo generation or modification of essential biomass components", PLoS ONE, vol. 8, no. 12, 2013. [abstract] [doi]
ABSTRACT: The success of genome-scale metabolic modeling is contingent on a model's ability to accurately predict growth and metabolic behaviors. To date, little focus has been directed towards developing systematic methods of proposing, modifying and interrogating an organism's biomass requirements that are used in constraint-based models. To address this gap, the biomass modification and generation (BioMog) framework was created and used to generate lists of biomass components de novo, as well as to modify predefined biomass component lists, for models of Escherichia coli (iJO1366) and of Shewanella oneidensis (iSO783) from high-throughput growth phenotype and fitness datasets. BioMog's de novo biomass component lists included, either implicitly or explicitly, up to seventy percent of the components included in the predefined biomass equations, and the resulting de novo biomass equations outperformed the predefined biomass equations at qualitatively predicting mutant growth phenotypes by up to five percent. Additionally, the BioMog procedure can quantify how many experiments support or refute a particular metabolite's essentiality to a cell, and it facilitates the determination of inconsistent experiments and inaccurate reaction and/or gene to reaction associations. To further interrogate metabolite essentiality, the BioMog framework includes an experiment generation algorithm that allows for the design of experiments to test whether a metabolite is essential. Using BioMog, we correct experimental results relating to the essentiality of thyA gene in E. coli, as well as perform knockout experiments supporting the essentiality of protoheme. With these capabilities, BioMog can be a valuable resource for analyzing growth phenotyping data and component of a model developer's toolbox.
[38]J. J. Hamilton, V. Dwivedi and J. L. Reed, "Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models", Biophysical Journal, vol. 105, no. 2, 2013, pp. 512–522. [abstract] [doi]
ABSTRACT: Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations. © 2013 Biophysical Society.
[37]C. Cotten, J. L. Reed, "Constraint-based strain design using continuous modifications (CosMos) of flux bounds finds new strategies for metabolic engineering", Biotechnology Journal, vol. 8, no. 5, 2013, pp. 595–604. [abstract] [doi]
ABSTRACT: In recent years, a growing number of metabolic engineering strain design techniques have employed constraint-based modeling to determine metabolic and regulatory network changes which are needed to improve chemical production. These methods use systems-level analysis of metabolism to help guide experimental efforts by identifying deletions, additions, downregulations, and upregulations of metabolic genes that will increase biological production of a desired metabolic product. In this work, we propose a new strain design method with continuous modifications (CosMos) that provides strategies for deletions, downregulations, and upregulations of fluxes that will lead to the production of the desired products. The method is conceptually simple and easy to implement, and can provide additional strategies over current approaches. We found that the method was able to find strain design strategies that required fewer modifications and had larger predicted yields than strategies from previous methods in example and genome-scale networks. Using CosMos, we identified modification strategies for producing a variety of metabolic products, compared strategies derived from Escherichia coli and Saccharomyces cerevisiae metabolic models, and examined how imperfect implementation may affect experimental outcomes. This study gives a powerful and flexible technique for strain engineering and examines some of the unexpected outcomes that may arise when strategies are implemented experimentally.
[36]C. Cotten, J. L. Reed, "Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models.", BMC bioinformatics, vol. 14, no. 1, 2013, pp. 32. [abstract] [doi]
ABSTRACT: BACKGROUND: Constraint-based modeling uses mass balances, flux capacity, and reaction directionality constraints to predict fluxes through metabolism. Although transcriptional regulation and thermodynamic constraints have been integrated into constraint-based modeling, kinetic rate laws have not been extensively used.$\backslash$n$\backslash$nRESULTS: In this study, an in vivo kinetic parameter estimation problem was formulated and solved using multi-omic data sets for Escherichia coli. To narrow the confidence intervals for kinetic parameters, a series of kinetic model simplifications were made, resulting in fewer kinetic parameters than the full kinetic model. These new parameter values are able to account for flux and concentration data from 20 different experimental conditions used in our training dataset. Concentration estimates from the simplified kinetic model were within one standard deviation for 92.7% of the 790 experimental measurements in the training set. Gibbs free energy changes of reaction were calculated to identify reactions that were often operating close to or far from equilibrium. In addition, enzymes whose activities were positively or negatively influenced by metabolite concentrations were also identified. The kinetic model was then used to calculate the maximum and minimum possible flux values for individual reactions from independent metabolite and enzyme concentration data that were not used to estimate parameter values. Incorporating these kinetically-derived flux limits into the constraint-based metabolic model improved predictions for uptake and secretion rates and intracellular fluxes in constraint-based models of central metabolism.$\backslash$n$\backslash$nCONCLUSIONS: This study has produced a method for in vivo kinetic parameter estimation and identified strategies and outcomes of kinetic model simplification. We also have illustrated how kinetic constraints can be used to improve constraint-based model predictions for intracellular fluxes and biomass yield and identify potential metabolic limitations through the integrated analysis of multi-omics datasets.
[35]T. T. Vu, S. M. Stolyar, G. E. Pinchuk, E. a. Hill, L. a. Kucek, R. N. Brown, M. S. Lipton, A. Osterman, J. K. Fredrickson, A. E. Konopka, A. S. Beliaev, J. L. Reed, "Genome-scale modeling of light-driven reductant partitioning and carbon fluxes in diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142", PLoS Computational Biology, vol. 8, no. 4, 2012. [abstract] [doi]
ABSTRACT: Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When growth is limited by the flux through photosystem I, terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant. Similarly, under photosystem II flux limitation, excess electron carriers must be removed via cyclic electron transport. Furthermore, in silico calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, which we used to further improve the resolution of intracellular flux values.
[34]C. J. Tervo, J. L. Reed, "FOCAL: an experimental design tool for systematizing metabolic discoveries and model development.", Genome Biology, vol. 13, no. 12, 2012, pp. R116. [abstract] [doi]
ABSTRACT: ABSTRACT: Current computational tools can generate and improve genome-scale models based on existing data; however, for many organisms, the data needed to test and refine such models is not available. To facilitate model development, we created the forced coupling algorithm, FOCAL, to identify genetic and environmental conditions such that a reaction becomes essential for an experimentally measurable phenotype. This reaction's conditional essentiality can then be tested experimentally to evaluate whether network connections occur or to create strains with desirable phenotypes. FOCAL allows network connections to be queried, which improves our understanding of metabolism and accuracy of developed models.
[33]M. S. Schwalbach, D. H. Keating, M. Tremaine, W. D. Marner, Y. Zhang, W. Bothfeld, A. Higbee, J. a. Grass, C. Cotten, J. L. Reed, L. D. C. Sousa, M. Jin, V. Balan, J. Ellinger, B. Dale, P. J. Kiley, R. Landick, "Complex physiology and compound stress responses during fermentation of alkali-pretreated corn stover hydrolysate by an Escherichia coli ethanologen", Applied and Environmental Microbiology, vol. 78, no. 9, 2012, pp. 3442–3457. [abstract] [doi]
ABSTRACT: The physiology of ethanologenic Escherichia coli grown anaerobically in alkali-pretreated plant hydrolysates is complex and not well studied. To gain insight into how E. coli responds to such hydrolysates, we studied an E. coli K-12 ethanologen fermenting a hydrolysate prepared from corn stover pretreated by ammonia fiber expansion. Despite the high sugar content (∼6% glucose, 3% xylose) and relatively low toxicity of this hydrolysate, E. coli ceased growth long before glucose was depleted. Nevertheless, the cells remained metabolically active and continued conversion of glucose to ethanol until all glucose was consumed. Gene expression profiling revealed complex and changing patterns of metabolic physiology and cellular stress responses during an exponential growth phase, a transition phase, and the glycolytically active stationary phase. During the exponential and transition phases, high cell maintenance and stress response costs were mitigated, in part, by free amino acids available in the hydrolysate. However, after the majority of amino acids were depleted, the cells entered stationary phase, and ATP derived from glucose fermentation was consumed entirely by the demands of cell maintenance in the hydrolysate. Comparative gene expression profiling and metabolic modeling of the ethanologen suggested that the high energetic cost of mitigating osmotic, lignotoxin, and ethanol stress collectively limits growth, sugar utilization rates, and ethanol yields in alkali-pretreated lignocellulosic hydrolysates.
[32]J. Schellenberger, D. C. Zielinski, W. Choi, S. Madireddi, V. Portnoy, D. a. Scott, J. L. Reed, A. L. Osterman, B. O. Palsson, "Predicting outcomes of steady-state 13C isotope tracing experiments using Monte Carlo sampling", BMC Systems Biology, vol. 6, no. 1, 2012, pp. 9. [abstract] [doi]
ABSTRACT: ABSTRACT: BACKGROUND: Carbon-13 (13C) analysis is a commonly used method for estimating reaction rates in biochemical networks. The choice of carbon labeling pattern is an important consideration when designing these experiments. We present a novel Monte Carlo algorithm for finding the optimal substrate input label for a particular experimental objective (flux or flux ratio). Unlike previous work, this method does not require assumption of the flux distribution beforehand. RESULTS: Using a large E. coli isotopomer model, different commercially available substrate labeling patterns were tested computationally for their ability to determine reaction fluxes. The choice of optimal labeled substrate was found to be dependent upon the desired experimental objective. Many commercially available labels are predicted to be outperformed by complex labeling patterns. Based on Monte Carlo Sampling, the dimensionality of experimental data was found to be considerably less than anticipated, suggesting that effectiveness of 13C experiments for determining reaction fluxes across a large-scale metabolic network is less than previously believed. CONCLUSIONS: While 13C analysis is a useful tool in systems biology, high redundancy in measurements limits the information that can be obtained from each experiment. It is however possible to compute potential limitations before an experiment is run and predict whether, and to what degree, the rate of each reaction can be resolved.
[31]J. L. Reed, "Shrinking the Metabolic Solution Space Using Experimental Datasets", PLoS Computational Biology, vol. 8, no. 8, 2012. [abstract] [doi]
ABSTRACT: Constraint-based models of metabolism have been used in a variety of studies on drug discovery, metabolic engineering, evolution, and multi-species interactions. These genome-scale models can be generated for any sequenced organism since their main parameters (i.e., reaction stoichiometry) are highly conserved. Their relatively low parameter requirement makes these models easy to develop; however, these models often result in a solution space with multiple possible flux distributions, making it difficult to determine the precise flux state in the cell. Recent research efforts in this modeling field have investigated how additional experimental data, including gene expression, protein expression, metabolite concentrations, and kinetic parameters, can be used to reduce the solution space. This mini-review provides a summary of the data-driven computational approaches that are available for reducing the solution space and thereby improve predictions of intracellular fluxes by constraint-based models.
[30]J. Kim, J. L. Reed, "RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations", Genome Biology, vol. 13, no. 9, 2012, pp. R78. [abstract] [doi]
ABSTRACT: Predicting cellular responses to perturbations is an important task in systems biology. We report a new approach, RELATCH, which uses flux and gene expression data from a reference state to predict metabolic responses in a genetically or environmentally perturbed state. Using the concept of relative optimality, which considers relative flux changes from a reference state, we hypothesize a relative metabolic flux pattern is maintained from one state to another, and that cells adapt to perturbations using metabolic and regulatory reprogramming to preserve this relative flux pattern. This constraint-based approach will have broad utility where predictions of metabolic responses are needed.
[29]J. Hamilton, J. Reed, "Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models", PLoS ONE, vol. 7, no. 4, 2012, pp. e34670. [abstract] [doi]
ABSTRACT: Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here.
[28]S. Imam, S. Yilmaz, U. Sohmen, A. S. Gorzalski, J. L. Reed, D. R. Noguera, T. J. Donohue, "iRsp1095: a genome-scale reconstruction of the Rhodobacter sphaeroides metabolic network.", BMC systems biology, vol. 5, no. 1, jan 2011, pp. 116. [abstract]
ABSTRACT: BACKGROUND: Rhodobacter sphaeroides is one of the best studied purple non-sulfur photosynthetic bacteria and serves as an excellent model for the study of photosynthesis and the metabolic capabilities of this and related facultative organisms. The ability of R. sphaeroides to produce hydrogen (H₂), polyhydroxybutyrate (PHB) or other hydrocarbons, as well as its ability to utilize atmospheric carbon dioxide (CO₂) as a carbon source under defined conditions, make it an excellent candidate for use in a wide variety of biotechnological applications. A genome-level understanding of its metabolic capabilities should help realize this biotechnological potential. RESULTS: Here we present a genome-scale metabolic network model for R. sphaeroides strain 2.4.1, designated iRsp1095, consisting of 1,095 genes, 796 metabolites and 1158 reactions, including R. sphaeroides-specific biomass reactions developed in this study. Constraint-based analysis showed that iRsp1095 agreed well with experimental observations when modeling growth under respiratory and phototrophic conditions. Genes essential for phototrophic growth were predicted by single gene deletion analysis. During pathway-level analyses of R. sphaeroides metabolism, an alternative route for CO₂ assimilation was identified. Evaluation of photoheterotrophic H2 production using iRsp1095 indicated that maximal yield would be obtained from growing cells, with this predicted maximum \~50% higher than that observed experimentally from wild type cells. Competing pathways that might prevent the achievement of this theoretical maximum were identified to guide future genetic studies. CONCLUSIONS: iRsp1095 provides a robust framework for future metabolic engineering efforts to optimize the solar- and nutrient-powered production of biofuels and other valuable products by R. sphaeroides and closely related organisms.
[27]I. Thiele, D. R. Hyduke, B. Steeb, G. Fankam, D. K. Allen, S. Bazzani, P. Charusanti, F. Chen, R. M. T. Fleming, C. a. Hsiung, S. C. J. De Keersmaecker, Y. Liao, K. Marchal, M. L. Mo, E. Özdemir, A. Raghunathan, J. L. Reed, S. Shin, S. Sigurbjörnsdóttir, J. Steinmann, S. Sudarsan, N. Swainston, I. M. Thijs, K. Zengler, B. O. Palsson, J. N. Adkins, D. Bumann, "A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2.", BMC systems biology, vol. 5, 2011, pp. 8. [abstract] [doi]
ABSTRACT: Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem.
[26]J. Kim, J. L. Reed and C. T. Maravelias, "Large-Scale Bi-Level strain design approaches and Mixed-Integer programming solution techniques", PLoS ONE, vol. 6, no. 9, 2011. [abstract] [doi]
ABSTRACT: The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.
[25]X. Chen, A. P. Alonso, D. K. Allen, J. L. Reed, Y. Shachar-Hill, "Synergy between 13C-metabolic flux analysis and flux balance analysis for understanding metabolic adaption to anaerobiosis in E. coli", Metabolic Engineering, vol. 13, no. 1, 2011, pp. 38–48. [abstract] [doi]
ABSTRACT: Genome-based Flux Balance Analysis (FBA) and steady-state isotopic-labeling-based Metabolic Flux Analysis (MFA) are complimentary approaches to predicting and measuring the operation and regulation of metabolic networks. Here, genome-derived models of Escherichia coli (E. coli) metabolism were used for FBA and 13C-MFA analyses of aerobic and anaerobic growths of wild-type E. coli (K-12 MG1655) cells. Validated MFA flux maps reveal that the fraction of maintenance ATP consumption in total ATP production is about 14% higher under anaerobic (51.1%) than aerobic conditions (37.2%). FBA revealed that an increased ATP utilization is consumed by ATP synthase to secrete protons from fermentation. The TCA cycle is shown to be incomplete in aerobically growing cells and submaximal growth is due to limited oxidative phosphorylation. An FBA was successful in predicting product secretion rates in aerobic culture if both glucose and oxygen uptake measurement were constrained, but the most-frequently predicted values of internal fluxes yielded from sampling the feasible space differ substantially from MFA-derived fluxes. © 2010 Elsevier Inc.
[24]D. J. Baumler, R. G. Peplinski, J. L. Reed, J. D. Glasner, N. T. Perna, "The evolution of metabolic networks of E. coli", BMC Systems Biology, vol. 5, no. 1, 2011, pp. 182. [abstract] [doi]
ABSTRACT: BACKGROUND: Despite the availability of numerous complete genome sequences from E. coli strains, published genome-scale metabolic models exist only for two commensal E. coli strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for E. coli strains could enhance these efforts.$\backslash$n$\backslash$nRESULTS: We used the genomic information from 16 E. coli strains to generate an E. coli pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for E. coli K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other E. coli strain models. All remaining orthologous groups were evaluated to see if new metabolic reactions could be added to generate a pangenome-scale metabolic model (iEco1712_pan). The pangenome model included reactions from a metabolic model update for E. coli K-12 MG1655 (iEco1339_MG1655) and enabled development of five additional strain-specific genome-scale metabolic models. These additional models include a second K-12 strain (iEco1335_W3110) and four pathogenic strains (two enterohemorrhagic E. coli O157:H7 and two uropathogens). When compared to the E. coli K-12 models, the metabolic models for the enterohemorrhagic (iEco1344_EDL933 and iEco1345_Sakai) and uropathogenic strains (iEco1288_CFT073 and iEco1301_UTI89) contained numerous lineage-specific gene and reaction differences. All six E. coli models were evaluated by comparing model predictions to carbon source utilization measurements under aerobic and anaerobic conditions, and to batch growth profiles in minimal media with 0.2% (w/v) glucose. An ancestral genome-scale metabolic model based on conserved ortholog groups in all 16 E. coli genomes was also constructed, reflecting the conserved ancestral core of E. coli metabolism (iEco1053_core). Comparative analysis of all six strain-specific E. coli models revealed that some of the pathogenic E. coli strains possess reactions in their metabolic networks enabling higher biomass yields on glucose. Finally the lineage-specific metabolic traits were compared to the ancestral core model predictions to derive new insight into the evolution of metabolism within this species.$\backslash$n$\backslash$nCONCLUSION: Our findings demonstrate that a pangenome-scale metabolic model can be used to rapidly construct additional E. coli strain-specific models, and that quantitative models of different strains of E. coli can accurately predict strain-specific phenotypes. Such pangenome and strain-specific models can be further used to engineer metabolic phenotypes of interest, such as designing new industrial E. coli strains.
[23]A. M. Wier, S. V. Nyholm, M. J. Mandel, R. P. Massengo-Tiassé, A. L. Schaefer, I. Koroleva, S. Splinter-Bondurant, B. Brown, L. Manzella, E. Snir, H. Almabrazi, T. E. Scheetz, M. D. F. Bonaldo, T. L. Casavant, M. B. Soares, J. E. Cronan, J. L. Reed, E. G. Ruby, M. J. McFall-Ngai, "Transcriptional patterns in both host and bacterium underlie a daily rhythm of anatomical and metabolic change in a beneficial symbiosis.", Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 5, 2010, pp. 2259–2264. [abstract] [doi]
ABSTRACT: Mechanisms for controlling symbiont populations are critical for maintaining the associations that exist between a host and its microbial partners. We describe here the transcriptional, metabolic, and ultrastructural characteristics of a diel rhythm that occurs in the symbiosis between the squid Euprymna scolopes and the luminous bacterium Vibrio fischeri. The rhythm is driven by the host's expulsion from its light-emitting organ of most of the symbiont population each day at dawn. The transcriptomes of both the host epithelium that supports the symbionts and the symbiont population itself were characterized and compared at four times over this daily cycle. The greatest fluctuation in gene expression of both partners occurred as the day began. Most notable was an up-regulation in the host of >50 cytoskeleton-related genes just before dawn and their subsequent down-regulation within 6 h. Examination of the epithelium by TEM revealed a corresponding restructuring, characterized by effacement and blebbing of its apical surface. After the dawn expulsion, the epithelium reestablished its polarity, and the residual symbionts began growing, repopulating the light organ. Analysis of the symbiont transcriptome suggested that the bacteria respond to the effacement by up-regulating genes associated with anaerobic respiration of glycerol; supporting this finding, lipid analysis of the symbionts' membranes indicated a direct incorporation of host-derived fatty acids. After 12 h, the metabolic signature of the symbiont population shifted to one characteristic of chitin fermentation, which continued until the following dawn. Thus, the persistent maintenance of the squid-vibrio symbiosis is tied to a dynamic diel rhythm that involves both partners.
[22]G. E. Pinchuk, E. a. Hill, O. V. Geydebrekht, J. de Ingeniis, X. Zhang, A. Osterman, J. H. Scott, S. B. Reed, M. F. Romine, A. E. Konopka, A. S. Beliaev, J. K. Fredrickson, J. L. Reed, "Constraint-based model of Shewanella oneidensis MR-1 metabolism: A tool for data analysis and hypothesis generation", PLoS Computational Biology, vol. 6, no. 6, 2010, pp. 1–8. [abstract] [doi]
ABSTRACT: Shewanellae are gram-negative facultatively anaerobic metal-reducing bacteria commonly found in chemically (i.e., redox) stratified environments. Occupying such niches requires the ability to rapidly acclimate to changes in electron donor/acceptor type and availability; hence, the ability to compete and thrive in such environments must ultimately be reflected in the organization and utilization of electron transfer networks, as well as central and peripheral carbon metabolism. To understand how Shewanella oneidensis MR-1 utilizes its resources, the metabolic network was reconstructed. The resulting network consists of 774 reactions, 783 genes, and 634 unique metabolites and contains biosynthesis pathways for all cell constituents. Using constraint-based modeling, we investigated aerobic growth of S. oneidensis MR-1 on numerous carbon sources. To achieve this, we (i) used experimental data to formulate a biomass equation and estimate cellular ATP requirements, (ii) developed an approach to identify cycles (such as futile cycles and circulations), (iii) classified how reaction usage affects cellular growth, (iv) predicted cellular biomass yields on different carbon sources and compared model predictions to experimental measurements, and (v) used experimental results to refine metabolic fluxes for growth on lactate. The results revealed that aerobic lactate-grown cells of S. oneidensis MR-1 used less efficient enzymes to couple electron transport to proton motive force generation, and possibly operated at least one futile cycle involving malic enzymes. Several examples are provided whereby model predictions were validated by experimental data, in particular the role of serine hydroxymethyltransferase and glycine cleavage system in the metabolism of one-carbon units, and growth on different sources of carbon and energy. This work illustrates how integration of computational and experimental efforts facilitates the understanding of microbial metabolism at a systems level.
[21]J. Kim, J. L. Reed, "OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains.", BMC systems biology, vol. 4, 2010, pp. 53. [abstract] [doi]
ABSTRACT: BACKGROUND: Computational modeling and analysis of metabolic networks has been successful in metabolic engineering of microbial strains for valuable biochemical production. Limitations of currently available computational methods for metabolic engineering are that they are often based on reaction deletions rather than gene deletions and do not consider the regulatory networks that control metabolism. Due to the presence of multi-functional enzymes and isozymes, computational designs based on reaction deletions can sometimes result in strategies that are genetically complicated or infeasible. Additionally, strains might not be able to grow initially due to regulatory restrictions. To overcome these limitations, we have developed a new approach (OptORF) for identifying metabolic engineering strategies based on gene deletion and overexpression. RESULTS: Here we propose an effective method to systematically integrate transcriptional regulatory networks and metabolic networks. This allows for the formulation of linear optimization problems that search for metabolic and/or regulatory perturbations that couple biomass and biochemical production, thus proposing adaptive evolutionary strain designs. Using genome-scale models of Escherichia coli, we have implemented the OptORF algorithm (which considers gene deletions and transcriptional regulation) and compared its metabolic engineering strategies for ethanol production to those found using OptKnock (which considers reaction deletions). Our results found that the reaction-based strategies often require more gene deletions to remove the identified reactions (2 more genes than reactions), and result in lethal growth phenotypes when transcriptional regulation is considered (162 out of 200 cases). Finally, we present metabolic engineering strategies for producing ethanol and higher alcohols (e.g. isobutanol) in E. coli using our OptORF approach. We have found common genetic modifications such as deletion of pgi and overexpression of edd, as well as chemical specific strategies for producing different alcohols. CONCLUSIONS: By taking regulatory effects into account, OptORF can propose changes such as the overexpression of metabolic genes or deletion of transcriptional factors, in addition to the deletion of metabolic genes, that may lead to faster evolutionary trajectories. While biofuel production in E. coli is evaluated here, the developed OptORF approach is general and can be applied to optimize the production of different compounds in other biological systems.
[20]D. Barua, J. Kim and J. L. Reed, "An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models", PLoS Computational Biology, vol. 6, no. 10, 2010. [abstract] [doi]
ABSTRACT: Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor–gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions.
[19]J. L. Reed, "Descriptive and predictive applications of constraint-based metabolic models", Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, vol. 2009, 2009, pp. 5460–5463. [abstract] [doi]
ABSTRACT: Constraint-based models of metabolism are becoming available for an increasing number of organisms. These models can be used in combination with existing experimental data to describe the behavior of an organism and to analyze experimental observations in the context of a model. Such a descriptive application of the models can also allow for the integration of various types of data. Additionally, these models can be used in a predictive fashion to hypothesize the outcomes of new experiments. Comparing model predictions with experimental results allows for the iterative improvement of developed models and increases our understanding of the organism being studied. A number of recent examples of both descriptive and predictive applications of constraint-based models are discussed.
[18]A. Raghunathan, J. Reed, S. Shin, B. Palsson, S. Daefler, "Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction.", BMC systems biology, vol. 3, 2009, pp. 38. [abstract] [doi]
ABSTRACT: BACKGROUND: Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction. RESULTS: We have developed and validated a genome-scale metabolic network of Salmonella typhimurium LT2 (iRR1083). This model accounts for 1,083 genes that encode proteins catalyzing 1,087 unique metabolic and transport reactions in the bacterium. We employed flux balance analysis and in silico gene essentiality analysis to investigate growth under a wide range of conditions that mimic in vitro and host cell environments. Gene expression profiling of S. typhimurium isolated from macrophage cell lines was used to constrain the model to predict metabolic pathways that are likely to be operational during infection. CONCLUSION: Our analysis suggests that there is a robust minimal set of metabolic pathways that is required for successful replication of Salmonella inside the host cell. This model also serves as platform for the integration of high-throughput data. Its computational power allows identification of networked metabolic pathways and generation of hypotheses about metabolism during infection, which might be used for the rational design of novel antibiotics or vaccine strains.
[17]A. M. Feist, M. J. Herrgå rd, I. Thiele, J. L. Reed, B. O. Palsson, "Reconstruction of biochemical networks in microorganisms.", Nature reviews. Microbiology, vol. 7, no. 2, 2009, pp. 129–143. [abstract] [doi]
ABSTRACT: Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.
[16]J. K. Fredrickson, M. F. Romine, A. S. Beliaev, J. M. Auchtung, M. E. Driscoll, T. S. Gardner, K. H. Nealson, A. L. Osterman, G. Pinchuk, J. L. Reed, D. a. Rodionov, J. L. M. Rodrigues, D. a. Saffarini, M. H. Serres, A. M. Spormann, I. B. Zhulin, J. M. Tiedje, "Towards environmental systems biology of Shewanella.", Nature reviews. Microbiology, vol. 6, no. 8, 2008, pp. 592–603. [abstract] [doi]
ABSTRACT: Bacteria of the genus Shewanella are known for their versatile electron-accepting capacities, which allow them to couple the decomposition of organic matter to the reduction of the various terminal electron acceptors that they encounter in their stratified environments. Owing to their diverse metabolic capabilities, shewanellae are important for carbon cycling and have considerable potential for the remediation of contaminated environments and use in microbial fuel cells. Systems-level analysis of the model species Shewanella oneidensis MR-1 and other members of this genus has provided new insights into the signal-transduction proteins, regulators, and metabolic and respiratory subsystems that govern the remarkable versatility of the shewanellae.
[15]O. Resendis-Antonio, J. L. Reed, S. Encarnación, J. Collado-Vides, B. Palsson, "Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli", PLoS Computational Biology, vol. 3, no. 10, 2007, pp. 1887–1895. [abstract] [doi]
ABSTRACT: Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation.
[14]A. M. Feist, C. S. Henry, J. L. Reed, M. Krummenacker, A. R. Joyce, P. D. Karp, L. J. Broadbelt, V. Hatzimanikatis, B. O. Palsson, "A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information.", Molecular systems biology, vol. 3, 2007, pp. 121. [abstract] [doi]
ABSTRACT: An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.
[13]J. L. Reed, T. R. Patel, K. H. Chen, A. R. Joyce, M. K. Applebee, C. D. Herring, O. T. Bui, E. M. Knight, S. S. Fong, B. O. Palsson, "Systems approach to refining genome annotation.", Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 46, 2006, pp. 17480–17484. [abstract] [doi]
ABSTRACT: Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, dctA, idnT, and putP) with two new enzymatic activities and four transport functions. This study thus demonstrates the use of systems analysis to discover metabolic and transport functions and their genetic basis by a combination of experimental and computational approaches.
[12]J. L. Reed, I. Famili, I. Thiele, B. O. Palsson, "Towards multidimensional genome annotation.", Nature reviews. Genetics, vol. 7, no. 2, 2006, pp. 130–141. [abstract] [doi]
ABSTRACT: Our information about the gene content of organisms continues to grow as more genomes are sequenced and gene products are characterized. Sequence-based annotation efforts have led to a list of cellular components, which can be thought of as a one-dimensional annotation. With growing information about component interactions, facilitated by the advancement of various high-throughput technologies, systemic, or two-dimensional, annotations can be generated. Knowledge about the physical arrangement of chromosomes will lead to a three-dimensional spatial annotation of the genome and a fourth dimension of annotation will arise from the study of changes in genome sequences that occur during adaptive evolution. Here we discuss all four levels of genome annotation, with specific emphasis on two-dimensional annotation methods.
[11]A. R. Joyce, J. L. Reed, A. White, R. Edwards, A. Osterman, T. Baba, H. Mori, S. a. Lesely, B. Palsson, S. Agarwalla, "Experimental and computational assessment of conditionally essential genes in Escherichia coli", Journal of Bacteriology, vol. 188, no. 23, 2006, pp. 8259–8271. [abstract] [doi]
ABSTRACT: Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these data sets have yet to be analyzed in the context of a genome scale model. Here, we present an integrative model-driven analysis of the Keio E. coli mutant collection screened in this study on glycerol-supplemented minimal medium. Out of 3,888 single-deletion mutants tested, 119 mutants were unable to grow on glycerol minimal medium. These conditionally essential genes were then evaluated using a genome scale metabolic and transcriptional-regulatory model of E. coli, and it was found that the model made the correct prediction in approximately 91% of the cases. The discrepancies between model predictions and experimental results were analyzed in detail to indicate where model improvements could be made or where the current literature lacks an explanation for the observed phenotypes. The identified set of essential genes and their model-based analysis indicates that our current understanding of the roles these essential genes play is relatively clear and complete. Furthermore, by analyzing the data set in terms of metabolic subsystems across multiple genomes, we can project which metabolic pathways are likely to play equally important roles in other organisms. Overall, this work establishes a paradigm that will drive model enhancement while simultaneously generating hypotheses that will ultimately lead to a better understanding of the organism.
[10]C. L. Barrett, C. D. Herring, J. L. Reed, B. O. Palsson, "The global transcriptional regulatory network for metabolism in Escherichia coli exhibits few dominant functional states.", Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 52, 2005, pp. 19103–19108. [abstract] [doi]
ABSTRACT: A principal aim of systems biology is to develop in silico models of whole cells or cellular processes that explain and predict observable cellular phenotypes. Here, we use a model of a genome-scale reconstruction of the integrated metabolic and transcriptional regulatory networks for Escherichia coli, composed of 1,010 gene products, to assess the properties of all functional states computed in 15,580 different growth environments. The set of all functional states of the integrated network exhibits a discernable structure that can be visualized in 3-dimensional space, showing that the transcriptional regulatory network governing metabolism in E. coli responds primarily to the available electron acceptor and the presence of glucose as the carbon source. This result is consistent with recently published experimental data. The observation that a complex network composed of 1,010 genes is organized to achieve few dominant modes demonstrates the utility of the systems approach for consolidating large amounts of genome-scale molecular information about a genome and its regulation to elucidate an organism's preferred environments and functional capabilities.
[9]J. L. Reed, B. Palsson, "Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: Assessment of correlated reaction subsets that comprise network states", Genome Research, vol. 14, no. 9, 2004, pp. 1797–1805. [abstract] [doi]
ABSTRACT: The constraint-based analysis of genome-scale metabolic and regulatory networks has been successful in predicting phenotypes and useful for analyzing high-throughput data sets. Within this modeling framework, linear optimization has been used to study genome-scale metabolic models, resulting in the enumeration of single optimal solutions describing the best use of the network to support growth. Here mixed-integer linear programming was used to calculate and study a subset of the alternate optimal solutions for a genome-scale metabolic model of Escherichia coli (iJR904) under a wide variety of environmental conditions. Analysis of the calculated sets of optimal solutions found that: (1) only a small subset of reactions in the network have variable fluxes across optima; (2) sets of reactions that are always used together in optimal solutions, correlated reaction sets, showed moderate agreement with the currently known transcriptional regulatory structure in E. coli and available expression data, and (3) reactions that are used under certain environmental conditions can provide clues about network regulatory needs. In addition, calculation of suboptimal flux distributions, using flux variability analysis, identified reactions which are used under significantly more environmental conditions suboptimally than optimally. Together these results demonstrate the utilization of reactions in genome-scale models under a variety of different growth conditions.
[8]N. D. Price, J. L. Reed and B. O. Palsson, "Genome-scale models of microbial cells: evaluating the consequences of constraints.", Nature reviews. Microbiology, vol. 2, no. 11, 2004, pp. 886–897. [abstract] [doi]
ABSTRACT: Microbial cells operate under governing constraints that limit their range of possible functions. With the availability of annotated genome sequences, it has become possible to reconstruct genome-scale biochemical reaction networks for microorganisms. The imposition of governing constraints on a reconstructed biochemical network leads to the definition of achievable cellular functions. In recent years, a substantial and growing toolbox of computational analysis methods has been developed to study the characteristics and capabilities of microorganisms using a constraint-based reconstruction and analysis (COBRA) approach. This approach provides a biochemically and genetically consistent framework for the generation of hypotheses and the testing of functions of microbial cells.
[7]J. a. Papin, J. L. Reed and B. O. Palsson, "Hierarchical thinking in network biology: The unbiased modularization of biochemical networks", Trends in Biochemical Sciences, vol. 29, no. 12, 2004, pp. 641–647. [abstract] [doi]
ABSTRACT: As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.
[6]M. W. Covert, E. M. Knight, J. L. Reed, M. J. Herrgard, B. O. Palsson, "Integrating high-throughput and computational data elucidates bacterial networks.", Nature, vol. 429, no. 6987, 2004, pp. 92–96. [abstract] [doi]
ABSTRACT: The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.
[5]J. L. Reed, B. O. Palsson, "Thirteen Years of Building Constraint-Based In Silico Models of Escherichia coli", Journal of Bacteriology, vol. 185, no. 9, may 2003, pp. 2692–2699.
[4]J. L. Reed, T. D. Vo, C. H. Schilling, B. O. Palsson, "An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR).", Genome biology, vol. 4, no. 9, jan 2003, pp. R54. [abstract]
ABSTRACT: BACKGROUND: Diverse datasets, including genomic, transcriptomic, proteomic and metabolomic data, are becoming readily available for specific organisms. There is currently a need to integrate these datasets within an in silico modeling framework. Constraint-based models of Escherichia coli K-12 MG1655 have been developed and used to study the bacterium's metabolism and phenotypic behavior. The most comprehensive E. coli model to date (E. coli iJE660a GSM) accounts for 660 genes and includes 627 unique biochemical reactions. RESULTS: An expanded genome-scale metabolic model of E. coli (iJR904 GSM/GPR) has been reconstructed which includes 904 genes and 931 unique biochemical reactions. The reactions in the expanded model are both elementally and charge balanced. Network gap analysis led to putative assignments for 55 open reading frames (ORFs). Gene to protein to reaction associations (GPR) are now directly included in the model. Comparisons between predictions made by iJR904 and iJE660a models show that they are generally similar but differ under certain circumstances. Analysis of genome-scale proton balancing shows how the flux of protons into and out of the medium is important for maximizing cellular growth. CONCLUSIONS: E. coli iJR904 has improved capabilities over iJE660a. iJR904 is a more complete and chemically accurate description of E. coli metabolism than iJE660a. Perhaps most importantly, iJR904 can be used for analyzing and integrating the diverse datasets. iJR904 will help to outline the genotype-phenotype relationship for E. coli K-12, as it can account for genomic, transcriptomic, proteomic and fluxomic data simultaneously.
[3]J. L. Reed, B. O. Palsson, "Thirteen Years of Building Constraint-Based In Silico Models of Escherichia coli MINIREVIEW Thirteen Years of Building Constraint-Based In Silico Models of Escherichia coli", Society, vol. 185, no. 9, 2003. [doi]
[2]N. D. Price, J. L. Reed, J. a. Papin, I. Famili, B. O. Palsson, "Analysis of metabolic capabilities using singular value decomposition of extreme pathway matrices.", Biophysical journal, vol. 84, no. 2 Pt 1, 2003, pp. 794–804. [abstract] [doi]
ABSTRACT: It is now possible to construct genome-scale metabolic networks for particular microorganisms. Extreme pathway analysis is a useful method for analyzing the phenotypic capabilities of these networks. Many extreme pathways are needed to fully describe the functional capabilities of genome-scale metabolic networks, and therefore, a need exists to develop methods to study these large sets of extreme pathways. Singular value decomposition (SVD) of matrices of extreme pathways was used to develop a conceptual framework for the interpretation of large sets of extreme pathways and the steady-state flux solution space they define. The key results of this study were: 1), convex steady-state solution cones describing the potential functions of biochemical networks can be studied using the modes generated by SVD; 2), Helicobacter pylori has a more rigid metabolic network (i.e., a lower dimensional solution space and a more dominant first singular value) than Haemophilus influenzae for the production of amino acids; and 3), SVD allows for direct comparison of different solution cones resulting from the production of different amino acids. SVD was used to identify key network branch points that may identify key control points for regulation. Therefore, SVD of matrices of extreme pathways has proved to be a useful method for analyzing the steady-state solution space of genome-scale metabolic networks.
[1]N. D. Price, J. L. Reed, J. a. Papin, S. J. Wiback, B. O. Palsson, "Network-based analysis of metabolic regulation in the human red blood cell", Journal of Theoretical Biology, vol. 225, no. 2, 2003, pp. 185–194. [abstract] [doi]
ABSTRACT: Reconstruction of cell-scale metabolic networks is now possible. A description of allowable metabolic network functions can be obtained using extreme pathways, which are the convex basis vectors of the solution space containing all steady state flux distributions. However, only a portion of these allowable network functions are physiologically possible due to kinetic and regulatory constraints. Methods are now needed that enable us to take a defined metabolic network and deduce candidate regulatory structures that control the selection of these physiologically relevant states. One such approach is the singular value decomposition (SVD) of extreme pathway matrices (P), which allows for the characterization of steady state solution spaces. Eigenpathways, which are the left singular vectors from the SVD of P, can be described and categorized by their biochemical function. SVD of P for the human red blood cell showed that the first five eigenpathways, out of a total of 23, effectively characterize all the relevant physiological states of red blood cell metabolism calculated with a detailed kinetic model. Thus, with five degrees of freedom the magnitude and nature of the regulatory needs are defined. Additionally, the dominant features of these first five eigenpathways described key metabolic splits that are indeed regulated in the human red blood cell. The extreme pathway matrix is derived directly from network topology and only knowledge of Vmax values is needed to reach these conclusions. Thus, we have implemented a network-based analysis of regulation that complements the study of individual regulatory events. This topological approach may provide candidate regulatory structures for metabolic networks with known stoichiometry but poorly characterized regulation. © 2003 Elsevier Ltd. All rights reserved.