Genome-scale Metabolic Network Analysis Research Articles

Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe.

Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.

Insight into the biotechnology potential of Alicyclobacillus tolerans from whole genome sequence analysis and genome-scale metabolic network modeling.

Extremophilic bacteria have numerous uncovered biotechnological potentials. Acidophilic bacteria are important iron oxidizers that are valuable in bioleaching and in studying extreme environments on earth and in space. Despite their obvious potential, little is known about the genetic traits that underpin their metabolic functions, which are equally poorly understood from a mechanistic perspective. Novel bioinformatics and computational biology pipelines can be used to analyze whole genomes to obtain insights into the phenotypic potential of organisms as well as develop a mathematical model representation of metabolism. Whole-genome sequence analysis and a genome-scale metabolic network model was curated for an iron-oxidizing bacterium initially isolated from an acid mine drainage in Turkey, previously identified as Alicyclobacillus tolerans. The genome contained a high proportion of genes for energy generation from carbohydrates, amino acids synthesis and conversion, nucleic acid metabolism and repair which contribute to robust adaption to their extreme environments. Several candidate genes for pyrite metabolism, iron uptake, regulation and storage, as well as genes for resistance to important heavy metals were annotated. A curated genome-scale metabolic network analysis accurately predicted facultative anaerobic growth, heterotrophic characteristics, and growth on a wide variety of carbon sources. This is the first in-depth in silico analysis of A. tolerans to the best of our knowledge which is expected to lay the groundwork for future research and drive innovations in environmental microbiology and biotechnological applications. The genomic data and mechanistic framework will have applications in biomining, synthetic geomicrobiology on earth, as well as for space exploration and settlement.

FindTargetsWEB: A User-Friendly Tool for Identification of Potential Therapeutic Targets in Metabolic Networks of Bacteria.

Background: Healthcare-associated infections (HAIs) are a serious public health problem. They can be associated with morbidity and mortality and are responsible for the increase in patient hospitalization. Antimicrobial resistance among pathogens causing HAI has increased at alarming levels. In this paper, a robust method for analyzing genome-scale metabolic networks of bacteria is proposed in order to identify potential therapeutic targets, along with its corresponding web implementation, dubbed FindTargetsWEB. The proposed method assumes that every metabolic network presents fragile genes whose blockade will impair one or more metabolic functions, such as biomass accumulation. FindTargetsWEB automates the process of identification of such fragile genes using flux balance analysis (FBA), flux variability analysis (FVA), extended Systems Biology Markup Language (SBML) file parsing, and queries to three public repositories, i.e., KEGG, UniProt, and DrugBank. The web application was developed in Python using COBRApy and Django. Results: The proposed method was demonstrated to be robust enough to process even non-curated, incomplete, or imprecise metabolic networks, in addition to integrated host-pathogen models. A list of potential therapeutic targets and their putative inhibitors was generated as a result of the analysis of Pseudomonas aeruginosa metabolic networks available in the literature and a curated version of the metabolic network of a multidrug-resistant P. aeruginosa strain belonging to a clone endemic in Brazil (P. aeruginosa ST277). Genome-scale metabolic networks of other gram-positive and gram-negative bacteria, such as Staphylococcus aureus, Klebsiella pneumoniae, and Haemophilus influenzae, were also analyzed using FindTargetsWEB. Multiple potential targets have been found using the proposed method in all metabolic networks, including some overlapping between two or more pathogens. Among the potential targets, several have been previously reported in the literature as targets for antimicrobial development, and many targets have approved drugs. Despite similarities in the metabolic network structure for closely related bacteria, we show that the method is able to selectively identify targets in pathogenic versus non-pathogenic organisms. Conclusions: This new computational system can give insights into the identification of new candidate therapeutic targets for pathogenic bacteria and discovery of new antimicrobial drugs through genome-scale metabolic network analysis and heterogeneous data integration, even for non-curated or incomplete networks.

Engineering NOG-pathway in Escherichia coli for poly-(3-hydroxybutyrate) production from low cost carbon sources

ABSTRACTPoly-(3-hydroxybutyrate) (P3HB) is a polyester with biodegradable and biocompatible characteristics suitable for bio-plastics and bio-medical use. In order to reduce the raw material cost, cheaper carbon sources such as xylose and glycerol were evaluated for P3HB production. We first conducted genome-scale metabolic network analysis to find the optimal pathways for P3HB production using xylose or glycerol respectively as the sole carbon sources. The results indicated that the non-oxidative glycolysis (NOG) pathway is important to improve the product yields. We then engineered this pathway into E. coli by introducing foreign phophoketolase enzymes. The results showed that the carbon yield improved from 0.19 to 0.24 for xylose and from 0.30 to 0.43 for glycerol. This further proved that the introduction of NOG pathway can be used as a general strategy to improve P3HB production.

Engineering Escherichia coli for poly-(3-hydroxybutyrate) production guided by genome-scale metabolic network analysis.

Poly-(3-hydroxybutyrate) (P3HB) is a promising biodegradable plastic synthesized from acetyl-CoA. One important factor affecting the P3HB production cost is the P3HB yield. Through flux balance analysis of an extended genome-scale metabolic network of E. coli, we found that the introduction of non-oxidative glycolysis pathway (NOG), a previously reported pathway enabling complete carbon conservation, can increase the theoretical carbon yield from 67% to 89%, equivalent to the theoretical mass yield from 0.48g P3HB/g glucose to 0.64g P3HB/g glucose. Based on this analysis result, we introduced phosphoketolase and enhanced the NOG pathway in E. coli. The mass yield in the engineered strain was increased from 0.16g P3HB/g glucose to 0.24g P3HB/g glucose. We further overexpressed pntAB to enhance the NADPH availability and down-regulated TCA cycle to divert more acetyl-CoA toward P3HB. The final construct accumulated 5.7g/L P3HB and reached a carbon yield of 0.43 (a mass yield of 0.31g P3HB/g glucose) in shake flask cultures in shake flask cultures. The introduction of NOG pathway could also be useful for improving yields of many other biochemicals derived from acetyl-coA.

A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

Metabolic engineering of Escherichia coli for poly(3-hydroxybutyrate) production via threonine bypass.

BackgroundPoly(3-hydroxybutyrate) (PHB), have been considered to be good candidates for completely biodegradable polymers due to their similar mechanical properties to petroleum-derived polymers and complete biodegradability. Escherichia coli has been used to simulate the distribution of metabolic fluxes in recombinant E. coli producing poly(3-hydroxybutyrate) (PHB). Genome-scale metabolic network analysis can reveal unexpected metabolic engineering strategies to improve the production of biochemicals and biofuels.ResultsIn this study, we reported the discovery of a new pathway called threonine bypass by flux balance analysis of the genome-scale metabolic model of E. coli. This pathway, mainly containing the reactions for threonine synthesis and degradation, can potentially increase the yield of PHB and other acetyl-CoA derived products by reutilizing the CO2 released at the pyruvate dehydrogenase step. To implement the threonine bypass for PHB production in E. coli, we deregulated the threonine and serine degradation pathway and enhanced the threonine synthesis, resulting in 2.23-fold improvement of PHB titer. Then, we overexpressed glyA to enhance the conversion of glycine to serine and activated transhydrogenase to generate NADPH required in the threonine bypass.ConclusionsThe result strain TB17 (pBHR68) produced 6.82 g/L PHB with the yield of 0.36 g/g glucose in the shake flask fermentation and 35.92 g/L PHB with the yield of 0.23 g/g glucose in the fed-batch fermentation, which was almost 3.3-fold higher than the parent strain. The work outlined here shows that genome-scale metabolic network analysis can reveal novel metabolic engineering strategies for developing efficient microbial cell factories.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-015-0369-3) contains supplementary material, which is available to authorized users.

MetaNET--a web-accessible interactive platform for biological metabolic network analysis.

BackgroundMetabolic reactions have been extensively studied and compiled over the last century. These have provided a theoretical base to implement models, simulations of which are used to identify drug targets and optimize metabolic throughput at a systemic level. While tools for the perturbation of metabolic networks are available, their applications are limited and restricted as they require varied dependencies and often a commercial platform for full functionality. We have developed MetaNET, an open source user-friendly platform-independent and web-accessible resource consisting of several pre-defined workflows for metabolic network analysis.ResultMetaNET is a web-accessible platform that incorporates a range of functions which can be combined to produce different simulations related to metabolic networks. These include (i) optimization of an objective function for wild type strain, gene/catalyst/reaction knock-out/knock-down analysis using flux balance analysis. (ii) flux variability analysis (iii) chemical species participation (iv) cycles and extreme paths identification and (v) choke point reaction analysis to facilitate identification of potential drug targets. The platform is built using custom scripts along with the open-source Galaxy workflow and Systems Biology Research Tool as components. Pre-defined workflows are available for common processes, and an exhaustive list of over 50 functions are provided for user defined workflows.ConclusionMetaNET, available at http://metanet.osdd.net, provides a user-friendly rich interface allowing the analysis of genome-scale metabolic networks under various genetic and environmental conditions. The framework permits the storage of previous results, the ability to repeat analysis and share results with other users over the internet as well as run different tools simultaneously using pre-defined workflows, and user-created custom workflows.

Computing Elementary Flux Modes Involving a Set of Target Reactions.

Elementary flux mode (EM) computation is an important tool in the constraint-based analysis of genome-scale metabolic networks. Due to the combinatorial complexity of these networks, as well as the advances in the level of detail to which they can be reconstructed, an exhaustive enumeration of all EMs is often not practical. Therefore, in recent years interest has shifted towards searching EMs with specific properties. We present a novel method that allows computing EMs containing a given set of target reactions. This generalizes previous algorithms where the set of target reactions consists of a single reaction. In the one-reaction case, our method compares favorably to the previous approaches. In addition, we present several applications of our algorithm for computing EMs containing two target reactions in genome-scale metabolic networks. A software tool implementing the algorithms described in this paper is available at https://sourceforge.net/projects/caefm.

Genetic variability of mutans streptococci revealed by wide whole-genome sequencing

BackgroundMutans streptococci are a group of bacteria significantly contributing to tooth decay. Their genetic variability is however still not well understood.ResultsGenomes of 6 clinical S. mutans isolates of different origins, one isolate of S. sobrinus (DSM 20742) and one isolate of S. ratti (DSM 20564) were sequenced and comparatively analyzed. Genome alignment revealed a mosaic-like structure of genome arrangement. Genes related to pathogenicity are found to have high variations among the strains, whereas genes for oxidative stress resistance are well conserved, indicating the importance of this trait in the dental biofilm community. Analysis of genome-scale metabolic networks revealed significant differences in 42 pathways. A striking dissimilarity is the unique presence of two lactate oxidases in S. sobrinus DSM 20742, probably indicating an unusual capability of this strain in producing H2O2 and expanding its ecological niche. In addition, lactate oxidases may form with other enzymes a novel energetic pathway in S. sobrinus DSM 20742 that can remedy its deficiency in citrate utilization pathway.Using 67 S. mutans genomes currently available including the strains sequenced in this study, we estimates the theoretical core genome size of S. mutans, and performed modeling of S. mutans pan-genome by applying different fitting models. An “open” pan-genome was inferred.ConclusionsThe comparative genome analyses revealed diversities in the mutans streptococci group, especially with respect to the virulence related genes and metabolic pathways. The results are helpful for better understanding the evolution and adaptive mechanisms of these oral pathogen microorganisms and for combating them.

Fast thermodynamically constrained flux variability analysis

Flux variability analysis (FVA) is an important tool to further analyse the results obtained by flux balance analysis (FBA) on genome-scale metabolic networks. For many constraint-based models, FVA identifies unboundedness of the optimal flux space. This reveals that optimal flux solutions with net flux through internal biochemical loops are feasible, which violates the second law of thermodynamics. Such unbounded fluxes may be eliminated by extending FVA with thermodynamic constraints. We present a new algorithm for efficient flux variability (and flux balance) analysis with thermodynamic constraints, suitable for analysing genome-scale metabolic networks. We first show that FBA with thermodynamic constraints is NP-hard. Then we derive a theoretical tractability result, which can be applied to metabolic networks in practice. We use this result to develop a new constraint programming algorithm Fast-tFVA for fast FVA with thermodynamic constraints (tFVA). Computational comparisons with previous methods demonstrate the efficiency of the new method. For tFVA, a speed-up of factor 30-300 is achieved. In an analysis of genome-scale metabolic networks in the BioModels database, we found that in 485 of 716 networks, additional irreversible or fixed reactions could be detected. Fast-tFVA is written in C++ and published under GPL. It uses the open source software SCIP and libSBML. There also exists a Matlab interface for easy integration into Matlab. Fast-tFVA is available from page.mi.fu-berlin.de/arnem/fast-tfva.html. Supplementary data are available at Bioinformatics online.

A reliable simulator for dynamic flux balance analysis

Dynamic flux balance analysis (DFBA) provides a platform for detailed design, control and optimization of biochemical process technologies. It is a promising modeling framework that combines genome-scale metabolic network analysis with dynamic simulation of the extracellular environment. Dynamic flux balance analysis assumes that the intracellular species concentrations are in equilibrium with the extracellular environment. The resulting underdetermined stoichiometric model is solved under the assumption of a biochemical objective such as growth rate maximization. The model of the metabolism is coupled with the dynamic mass balance equations of the extracellular environment via expressions for the rates of substrate uptake and product excretion, which imposes additional constraints on the linear program (LP) defined by growth rate maximization of the metabolism. The linear program is embedded into the dynamic model of the bioreactor, and together with the additional constraints this provides an accurate model of the substrate consumption, product secretion, and biomass production during operation. A DFBA model consists of a system of ordinary differential equations for which the evaluation of the right-hand side requires not only function evaluations, but also the solution of one or more linear programs. The numerical tool presented here accurately and efficiently simulates large-scale dynamic flux balance models. The main advantages that this approach has over existing implementation are that the integration scheme has a variable step size, that the linear program only has to be solved when qualitative changes in the optimal flux distribution of the metabolic network occur, and that it can reliably simulate behavior near the boundary of the domain where the model is defined. This is illustrated through large-scale examples taken from the literature.

I AK692: A genome-scale metabolic model of Spirulina platensis C1

BackgroundSpirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438) genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium.ResultsIn this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP) analysis.ConclusionsThis study proposes the first genome-scale model of S. platensis C1, iAK692, which is a predictive metabolic platform for a global understanding of physiological behaviors and metabolic engineering. This platform could accelerate the integrative analysis of various “-omics” data, leading to strain improvement towards a diverse range of desired industrial products from Spirulina.

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS.

F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

BackgroundFlux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions.ResultsWe present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner.ConclusionsWe propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.

Metabolic Networks of Sodalis glossinidius: A Systems Biology Approach to Reductive Evolution

BackgroundGenome reduction is a common evolutionary process affecting bacterial lineages that establish symbiotic or pathogenic associations with eukaryotic hosts. Such associations yield highly reduced genomes with greatly streamlined metabolic abilities shaped by the type of ecological association with the host. Sodalis glossinidius, the secondary endosymbiont of tsetse flies, represents one of the few complete genomes available of a bacterium at the initial stages of this process. In the present study, genome reduction is studied from a systems biology perspective through the reconstruction and functional analysis of genome-scale metabolic networks of S. glossinidius.ResultsThe functional profile of ancestral and extant metabolic networks sheds light on the evolutionary events underlying transition to a host-dependent lifestyle. Meanwhile, reductive evolution simulations on the extant metabolic network can predict possible future evolution of S. glossinidius in the context of genome reduction. Finally, knockout simulations in different metabolic systems reveal a gradual decrease in network robustness to different mutational events for bacterial endosymbionts at different stages of the symbiotic association.ConclusionsStoichiometric analysis reveals few gene inactivation events whose effects on the functionality of S. glossinidius metabolic systems are drastic enough to account for the ecological transition from a free-living to host-dependent lifestyle. The decrease in network robustness across different metabolic systems may be associated with the progressive integration in the more stable environment provided by the insect host. Finally, reductive evolution simulations reveal the strong influence that external conditions exert on the evolvability of metabolic systems.

An extended bioreaction database that significantly improves reconstruction and analysis of genome-scale metabolic networks

The bioreaction database established by Ma and Zeng (Bioinformatics, 2003, 19, 270-277) for in silico reconstruction of genome-scale metabolic networks has been widely used. Based on more recent information in the reference databases KEGG LIGAND and Brenda, we upgrade the bioreaction database in this work by almost doubling the number of reactions from 3565 to 6851. Over 70% of the reactions have been manually updated/revised in terms of reversibility, reactant pairs, currency metabolites and error correction. For the first time, 41 spontaneous sugar mutarotation reactions are introduced into the biochemical database. The upgrade significantly improves the reconstruction of genome scale metabolic networks. Many gaps or missing biochemical links can be recovered, as exemplified with three model organisms Homo sapiens, Aspergillus niger, and Escherichia coli. The topological parameters of the constructed networks were also largely affected, however, the overall network structure remains scale-free. Furthermore, we consider the problem of computing biologically feasible shortest paths in reconstructed metabolic networks. We show that these paths are hard to compute and present solutions to find such paths in networks of small and medium size.

Genome-scale metabolic network analysis of multi-drug resistant pathogen Acinetobacter baumannii AYE for drug targeting

Genome-scale metabolic network analysis of multi-drug resistant pathogen Acinetobacter baumannii AYE for drug targeting

Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE

Acinetobacter baumannii has emerged as a new clinical threat to human health, particularly to ill patients in the hospital environment. Current lack of effective clinical solutions to treat this pathogen urges us to carry out systems-level studies that could contribute to the development of an effective therapy. Here we report the development of a strategy for identifying drug targets by combined genome-scale metabolic network and essentiality analyses. First, a genome-scale metabolic network of A. baumannii AYE, a drug-resistant strain, was reconstructed based on its genome annotation data, and biochemical knowledge from literatures and databases. In order to evaluate the performance of the in silico model, constraints-based flux analysis was carried out with appropriate constraints. Simulations were performed from both reaction (gene)- and metabolite-centric perspectives, each of which identifies essential genes/reactions and metabolites critical to the cell growth. The gene/reaction essentiality enables validation of the model and its comparative study with other known organisms' models. The metabolite essentiality approach was undertaken to predict essential metabolites that are critical to the cell growth. The EMFilter, a framework that filters initially predicted essential metabolites to find the most effective ones as drug targets, was also developed. EMFilter considers metabolite types, number of total and consuming reaction linkage with essential metabolites, and presence of essential metabolites and their relevant enzymes in human metabolism. Final drug target candidates obtained by this system framework are presented along with implications of this approach.

The genome-scale metabolic network analysis of Zymomonas mobilis ZM4 explains physiological features and suggests ethanol and succinic acid production strategies

BackgroundZymomonas mobilis ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, via the Entner-Doudoroff pathway. However, systems metabolic engineering is required to further enhance its metabolic performance for industrial application. As an important step towards this goal, the genome-scale metabolic model of Z. mobilis is required to systematically analyze in silico the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions.ResultsThe genome-scale metabolic model of Z. mobilis ZM4, ZmoMBEL601, was reconstructed based on its annotated genes, literature, physiological and biochemical databases. The metabolic model comprises 579 metabolites and 601 metabolic reactions (571 biochemical conversion and 30 transport reactions), built upon extensive search of existing knowledge. Physiological features of Z. mobilis were then examined using constraints-based flux analysis in detail as follows. First, the physiological changes of Z. mobilis as it shifts from anaerobic to aerobic environments (i.e. aerobic shift) were investigated. Then the intensities of flux-sum, which is the cluster of either all ingoing or outgoing fluxes through a metabolite, and the maximum in silico yields of ethanol for Z. mobilis and Escherichia coli were compared and analyzed. Furthermore, the substrate utilization range of Z. mobilis was expanded to include pentose sugar metabolism by introducing metabolic pathways to allow Z. mobilis to utilize pentose sugars. Finally, double gene knock-out simulations were performed to design a strategy for efficiently producing succinic acid as another example of application of the genome-scale metabolic model of Z. mobilis.ConclusionThe genome-scale metabolic model reconstructed in this study was able to successfully represent the metabolic characteristics of Z. mobilis under various conditions as validated by experiments and literature information. This reconstructed metabolic model will allow better understanding of Z. mobilis metabolism and consequently designing metabolic engineering strategies for various biotechnological applications.