Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment.Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including E. coli, S. cerevisiae and M. tuberculosis.Here, we present Acorn, an open source (GNU GPL) grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users) increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a web server allowing constraint based simulations of the genome scale metabolic reaction networks of Acorn is a free software package, which can be installed by research groups to create a web based environment for computer simulations of genome scale metabolic reaction networks. It facilitates shared access to models and creation of publicly available constraint based modelling resources. The acquisition of this information can be initiared by analysis of the genome sequence of interest, and this approach has been applied to the reconstruction of genome scale metabolic reaction networks (GSMNs) for major model organisms [Analysis of biochemical reaction networks, to enable predictions about the phenotype from a catalogue of molecular parts encoded in the genome, fulfils a major objective of Systems Biology. Due to the scarcity of detailed quantitative information about individual enzyme rate constants and intracellular concentrations, genome scale models have, so far, only been constructed for the special case of the metabolic reaction networks operating under steady state conditions. These models capitalize on the fact that metabolic conversions occur in the cell with very high rates and therefore, for the given set of genes active under experimental conditions of interest, the network of metabolic reactions can be considered to be in a quasi-steady state (where metabolite concentrations do not change). The system is then modelled by a set of linear relationships between the calculated reaction fluxes. Although, the linear system corresponding to genome scale models is underdetermined it can be exploited by Linear Programming (LP) based optimization of the objective function that represents the metabolic capability of interest. The objective function is defined as the flux through a specific reaction or the flux towards a specific metabolite. LP optimization results in a unique value for the objective function, but the corresponding flux distribution is not unique. The range of each individual reaction flux in all flux distributions that are consistent with the maximal objective function value can be exploited by the iterative LP protocols, described in the following sections. This approach is referred to as constraint based , becauserganisms -5, pathorganisms -8 and mirganisms ,10. The rganisms  and is crganisms ,13.The availability of full genome sequences has motivated the development of functional genomics internet portals, where a multiplicity of sequence-derived, high-throughput and traditional experimental data are integrated through appropriate comparative analysis protocols. The integration of constraint-based simulations of GSMNs with these portals will equip users, for the first time, with tools that will enable predictions of the metabolic phenotype of cells under defined experimental conditions, determined in terms of a set of nutrients present in the medium.The concept of gene essentiality provides a good illustration of these new capabilities. Currently, functional genomics portals describe each gene in terms of comparison to similar sequences in other organisms, direct experimental evidence about the function under particular experimental conditions and high throughput datasets in which the expression of the gene has been observed. A constraint-based analysis of the appropriate GSMN, integrated with this system, would allow this content to be dependent on experimental conditions, specified by the user. The user could specify the composition of the growth medium and then compute the function of the gene expressed as a set of metabolic capabilities for which this gene is essential under user-defined experimental conditions.Mycobacterium tuberculosis, the causative agent of Tuberculosis. In this case, the web interface allows the specification of the growth medium composition, and using this information, provides a calculation of the maximal theoretical flux towards biomass as well as a gene essentiality prediction. It also implements a reaction essentiality scan and Flux Variability Analysis (FVA) [Escherichia coli iJR904 model. The server displays metabolic pathway maps and overlays simulation results on these diagrams. Only a single evaluation of the objective function is provided and no iterative simulation approaches are available. We also note that none of the web servers described above are available as software that can be downloaded and used to create new web servers at the users institution. Recently, the BioMet Toolbox [So far, there have been the following attempts at creating web-based resources, allowing computation with GSMN models with subsequent linking of models and results to functional genomics resources. The GSMN-TB  is the fis (FVA) , which ais (FVA) , a functis (FVA)  system ais (FVA)  or Pathwis (FVA)  softwareis (FVA)  server a Toolbox  server hin silico experiments by specifying nutritional conditions and run virtual experiments in order to calculate the maximum theoretical value of the objective function, identify essential reactions, predict gene essentiality and perform Flux Variability Analysis (FVA) [Saccharomyces cerevisiae, E. coli and M. tuberculosis :In this work we present Acorn, a grid computing system, which can be deployed at the users institution and used to create web based resources for constraint based simulations of GSMN models. Research groups can install Acorn, set up user accounts and import models in the SBML format . Users cis (FVA) . Models http://sysbio3.fhms.surrey.ac.uk:8080/acorn/homepage.jsf.In this section we describe the implementation details of Acorn. We also explain and justify the technological and architectural choices made, and the design approaches applied to the implementation of the system.The Acorn system consists of three components Figure :1. The main application, which includes a web based user interface for defining and executing the required computational task as well a database for storing models, experiments and experimental results,2. The worker console application for performing the computations requested by users via the web interface,3. A graphical editor desktop application for defining which parts of the model are to be visualized and how.The user interface of the main application is web based, designed with ergonomics in mind and optimised for handling of large models. As an example of this latter feature, the user can quickly filter long lists with an AJAX (Asynchronous JavaScript and XML) based mechanism, i.e., without the delays that might be incurred in transferring data between the browser and the web server. An example of the ergonomic approach is the positioning of key control buttons such that they are always visible in the same place on the screen using specially defined CSS (Cascading Style Sheets). This facilitates working with long lists. A useful outcome of this approach is that the user does not have to scroll to start the simulation after accepting the choice of optimisation target on the reaction list.Since computations with Acorn can be time intensive, we have chosen an architecture Figure  that allhttps://glassfish.dev.java.net/. Acorn profits from the maturity and enterprise features of Java EE component technologies. For example, operations on the queue and database are subject to transaction control, so the computations can be consistently completed in situations like concurrent execution of many workers, failure of an active worker or restart of the main application. The GlassFish server is also equipped with necessary tools for monitoring and tuning. Furthermore, the flexibility of the enterprise programming environment allows the deployment of Acorn with any relational database management system providing JDBC drivers. The current version has been tested with Java Derby http://db.apache.org/derby/ and MySQL http://www.mysql.com databases.The system is implemented in Java with the use of the Java Enterprise Edition (Java EE) technology stack. The main application can be deployed on a Java EE 5.0 compatible application server as the Open Source GlassFish 2.1 http://www.gnu.org/software/glpk/. The linear programming engine is a binary executable file implemented in C++. The worker starts the linear programming engine using the operating system interface of Java, which sends the linear programming problem to its standard input and retrieves results from standard output without creating any temporary files. The engine is also available for users as a standalone command line tool, which can be used to implement other interfaces.Linear programming calculations are performed using the appropriate functions of the GLPK library http://platform.netbeans.org/. It runs on all platforms where the Java runtime environment is available. It accesses the main application to obtain the model data and to store defined visualisations for presentation in the web interface. This communication is performed though a Web Service provided by the main application, which, for the user, has the added advantage that it will not be blocked by firewalls.The graphical editor is a standalone Java desktop application built on the NetBeans Platform, which is a generic framework for Swing applications http://code.google.com/p/a-c-o-r-n/. The main application can be installed on any platform running Java VM, the Glassfish application server and a relational database management system. The binaries of the linear programming engine are available for Linux, MacOS × and Windows operating systems and, together with the Java VM, are necessary to run the worker. The graphical editor desktop application can be used on any system running Java VM. The current version of Acorn provides the following features.Distribution of Acorn can be implemented by downloading from Google Code repository The user logs into the system via web interface and can browse GSMN models in tabular format Figure . The rowThe following computational methods are available:i) Flux Balance Analysis (FBA) - calculation of the maximal theoretical value of the objective function under nutritional conditions specified by the user;iI) Gene essentiality prediction - the fluxes through all reactions which require the functionality of a selected gene are constrained to 0 and the maximal theoretical value of the objective function is calculated. If the objective function value equals 0, the gene is declared to be essential for particular metabolic function;iii) Reaction essentiality scan - each reaction, in turn, is inactivated and the FBA optimisation is run. The result lists the maximal value of the objective function for each single reaction knock-out;iv) Flux Variability Analysis (FVA)  - the flin silico experiment. Users can share in silico experiments and visualise their results on pathway diagrams.To run a simulation via the Acorn web interface the user first selects one of the methods and then specifies the objective function and nutritional conditions. The objective function can be specified by a metabolite name  or reaction name . Nutritional conditions are defined by constraining lower and upper bounds of transport reaction fluxes. The Acorn interface allows the specification of bounds for all reactions in the system, allowing for more complex analysis scenarios such as incorporation of internal metabolic flux measurements or simulations of multiple gene knockouts. The keyword searches can be used to quickly find reactions and metabolites of interest. Simulations are scheduled and the user can either wait for the completion of computational tasks, or log out and return to the system later to examine results. The simulation scenario, together with the simulation results are saved into the database as an A single execution of FBA is very computationally efficient. For example, calculation of the maximal growth rate for yeast GSMN model iND750 takes 0.73s on 2.13 GHz Intel Core 2 Duo processor CPU when calculated with the linear programming engine of the Acorn system. However, iterative protocols of reaction essentiality scans and FVA require one or two (respectively) evaluations of the objective function for each reaction in the system. Therefore, execution of FVA for the yeast GSMN model containing 1265 reactions takes about 15 minutes of CPU time. This computational time can be substantial for web-based applications, especially if multiple users are simultaneously using the server. The Acorn server, on the other hand, is using grid-based solutions to distribute computational load between multiple computers. The worker program is installed on each computer in the cluster. The worker connects to the Acorn server and receives a set of FBA objective function evaluation tasks to execute. Results of these tasks are sent back to the server (see Implementation section for more details). Therefore, FBA calculations from the Acorn server can be distributed over an arbitrary number of computers in the local area network or the internet, rendering the system scalable to large number of users and computational tasks.The user can import GSMN models in the SBML format Figure . The reain silico experiments with a particular model initially belong the user who created the model. The user may chose to share models and in silico experiments with other users in the system. This allows for the creation of online communities where users share models and results of numerical simulations. The models and experiments can be also made available to a publicly accessible account, which enables creation of web based resources for constraint based simulations of GSMN models.Every new user of Acorn has to register via the web interface. The first user registered, after the software installation step, is given administration privileges. The models are created during SBML file upload, and initially can be only used by the user who uploaded the SBML file. Likewise, Acorn provides a desktop editor for metabolic pathway diagrams Figure . DiagramNumerical results of FBA and FVA calculations can be visualised on pathway diagrams Figure . The patThe computational engine of the Acorn software is available as a stand-alone tool. The tool can be used to evaluate the objective function of the GSMN model written in simple text file format. The command line tool interprets boolean gene-reaction association formulas and can be therefore used to perform evaluate the objective function for a single gene knockout version of the model. Availability of the Acorn computational engine in the form of a command line tool binary is useful for the users who intend to develop their own interfaces. The tool could be easily integrated with the script languages used by the bioinformatics community and then become a part of an annotation pipeline. It can be also used to create graphics user interfaces other than Acorn. Finally, users of the Acorn web server can also use the command line tool to run some of their computations on their local machines if the resources of the web server are limited or constrained.M. tuberculosis [E. coli iAF1260 [S. cerevisiae iND750 [To demonstrate the features of Acorn, we have constructed a web resource for constraint-based simulation of the GSMNs of interest to our research group. The resource contains the GSMN-TB model of rculosis , reconste iND750 . The iAFe iND750 . In the E. coli model is linked to KEGG database, and the S. cerevisiae model is linked to the SGD database. These implementations have demonstrated that the model upload mechanism of Acorn can be used to link reaction lists and results files of GSMN models to major functional genomics resources, thus facilitating interpretation of computer simulations in the context of the wealth of knowledge and comparative analysis results available within these knowledge bases.Each of the models available within the web resource is linked to the relevant functional genomics portal. The gene names in GSMN-TB model are linked to the Tuberculist database, the The user can use our web resource to run all simulation protocols implemented in Acorn on the models mentioned above. To demonstrate the visualisation functionality of Acorn, we have used the desktop editor to create a layout of glycolytic pathway in Yeast and uploaded this layout into the iND750 model. Users can now visualise fluxes calculated by computer simulation on the pathway layouts of their choice. The fluxes are obtained by the calculations involving all reactions of the entire GSMN, but only the reactions of interest to the users are displayed in a format, which is the most informative for them.In this paper, we present the Acorn software package, which can be deployed at the user's institution and used to create web-based resources for constraint based simulations of GSMNs. Our software can be used to make a collection of GSMNs available to a community of users who hold accounts within the system. Users can upload models and run constraint-based simulation methods to make predictions about the metabolic capabilities of organisms under investigation. The models and results are linked to the functional genomic resources through the gene names. Therefore, the results of constraint based calculations can be examined in the context of genome sequences, annotations and sequence similarity-based predictions available in these portals. The grid-based architecture of Acorn allows for the implementation of computationally expensive simulation protocols requiring the execution of thousands of linear programming evaluations for the entire model. Users can visualise numerical results of simulation methods on pathway maps loaded into the system. These maps can represent tthe users view, a part of the model of specific interest to the user concerned, while numerical data visualised on the map are calculated by constraint-based modelling of the entire network. Pathway diagrams are constructed with the desktop tool, which supports rapid assignment of the graphical symbols to substances and reactions present in the model.One of the most important features of Acorn is user management and model sharing. Select users can upload starting models and share them with others. All other users can create variations of those starting models by modifying their parameters and set their own simulations without affecting the models and simulations run by other users. They may then choose to render the models, simulation scenarios and results visible to other users in the system and, through the guest user account, available to the research community. Therefore, Acorn can be used to create research community websites dedicated to the annotation and investigation of metabolic reaction network models.S. cerevisiae, E. coli, and M. tuberculosis. Users can either use a guest account or register and gain access to these models without the need of installing and learning modelling environments such as, for example, MATLAB based COBRA toolkits. Users can also use our server to upload their models, if they are represented in the SBML format, and use computational tools implemented within the system to study those models (and share the results with other users).In addition to the software that can be used to create a constraint-based modelling server, we also provide a web resource giving access to the GSMN models of We believe that Acorn provides several advantages and alternative solutions compared to existing web servers for constrained-based modelling of metabolic networks. It provides advantages when compared to the GSMN-TB server with respect to visualisation and implements a more convenient interface for model specification. Its main advantage over CycSim and WEbcoli is the implementation of the FVA and reaction essentiality scans. FVA is currently the only method, which allows the exploration of internal flux distribution in GSMN.s It is efficiently implemented in Acorn, due to its grid based architecture. Acorn also provides an alternative mechanism for visualising metabolic pathway maps. While CyCsim uses standard KEGG and BioCyc maps, the Acorn server allows the individual user to create their own views, which are linked with the model. Another major advantage of the Acorn environment over CycSim and WEbcoli is that it provides source code distribution, which can be used to create collaborative model development environments and public web resources in other laboratories.in silico experiments. This will facilitate improvement of metabolic models used in the identification of novel drug targets in microbial pathogens and metabolic engineering of industrially valuable microbial strains.To summarise, we believe that the Acorn server will be very useful for research groups reconstructing and studying GSMNs. Its application will result in establishment of online communities sharing models and results of Project name: Acornhttp://code.google.com/p/a-c-o-r-n/Project home page: http://sysbio3.fhms.surrey.ac.uk:8080/acorn/homepage.jsfSurrey GSMN server: Operating systems(s): Platform independentProgramming language: Java, C++Other requirements: Java 6.0 or higher, GlassFish Java EE Server v2 or higherLicense: GNU GPL v2Any restrictions to use by non-academics: The use of constraint based modelling results obtained by using iAF1260 and iND750 models on Surrey FBA resource is subjected to the BiGG database license.AMK and JS designed software functionality. JS designed software architecture, supervised software development and participated in software implementation. LBK, SG, DL, JL implemented database and worker modules. MM implemented pathway visualisation. JL implemented command line computational engine. CAR developed case studies for visualisation of yeast pathways. MB and JMF tested software functionality and participated in writing of the manuscript. AMK and JS wrote the manuscript. All authors read and approved the final manuscript.


The cellular response to such redox changes and the additional reducing power available to the cell can be harnessed to produce desired metabolites. In the context of microbial fermentation, these bioelectrochemical techniques can be used to improve product yields and/or productivity.A critical concern in metabolic engineering is the need to balance the demand and supply of redox intermediates such as NADH. Bioelectrochemical techniques offer a novel and promising method to alleviate redox imbalances during the synthesis of biochemicals and biofuels. Broadly, these techniques reduce intracellular NADE. coli. The results in this paper elucidate the role of bioelectrosynthesis and its impact on biomass growth, cellular ATP yields and biochemical production. The results also suggest that strain design strategies can change for fermentation processes that employ microbial electrosynthesis and suggest that dynamic operating strategies lead to maximizing productivity.We have developed a method to characterize the role of bioelectrosynthesis in chemical production using the genome-scale metabolic model of The results in this paper provide a systematic understanding of the benefits and limitations of bioelectrochemical techniques for biochemical production and highlight how electrical enhancement can impact cellular metabolism and biochemical production. Escherichia coli and Saccharomyces cerevisiae, is required to achieve desired process objectives  [In response to economic and environmental considerations, there has been increased interest in the development of commercial bioprocesses that produce biofuels and specialty chemicals. The successful commercialization of these bioprocesses requires that the bioproducts which are converted from biomass based feedstocks are produced at sufficiently high yields and productivity so that the process is economically viable . Hence, d titer) -4. Metabd titer) ,5.n-butanol, and succinate in a variety of hosts including S. cerevisiae, Clostridium acetobutylicum, Actinobacillus succinogenes [Because redox cofactors such as NADH or NADPH play an important role in cellular metabolism, altering the cellular redox balance has been regarded as an essential initial step in metabolic engineering for achieving bioprocess objectives . Geneticinogenes -13 Otherinogenes ,15.et al. were among the first to use these techniques to increase product synthesis by showing that it was possible to improve L-glutamic acid yields [Geobacter sulfurreducens [et al. demonstrated a proof of concept for this approach; Sporomusa ovata was used to generate acetate and small amounts of organic compounds by reducing carbon dioxide with an electrode powered by solar energy [S. ovata to develop strains that could produce valuable chemicals instead of acetate.These types of bioelectrochemical techniques, which refer to electricity driven product synthesis, are generally known as microbial electrosynthesis or bioelectrosynthesis . In 1979d yields . Consided yields . In 2004reducens . Recentlin silico genome scale models and computational algorithms such as Opt-Knock, OptForce and EMILiO that identify knockout, overexpression and/or inhibition strategies [The identification of genetic perturbations to a metabolic network has been aided by rategies -22. CurrE. coli as an example; (2) examine the role of microbial electrosynthesis on biomass growth; (3) examine the impact on biochemical production for a suite of top value-added chemicals; (4) identify which conditions microbial electrosynthesis is best suited as a tool to improve yield; and (5) comprehensively evaluate how microbial electrosynthesis may impact strain design and process productivity. These results provide valuable insights on the role of microbial electrosynthesis and highlight the need for additional studies to optimize this process.The lack of such a framework means that there is also a lack of systematic understanding of the instances where bioelectrosynthesis can lead to improved product yield . Hence, in order to characterize the role of electrical enhancement on chemical production, in this study we (1) develop a method to analyze the impact of microbial electrosynthesis on biochemical production using the genome-scale metabolism of Escherichia coli was used as the basis for all in silico evaluations [E. coli are well established and it is used widely in industry as platform for biochemical production. Even though E. coli is not a natural electricigen in the way that Geobacter or Shewanella are, E. coli can be electroactive and interact with an electrode in the presence of mediators [The iAF1260 metabolic reconstruction of luations . The iAFediators ,25E. coli was shown to reduce solid α-Fe2O3 in the absence of mediators after portions of the extracellular electron transfer chain of Shewanella oneidensis was expressed in E. coli [Geobacter sulfurreducens, suggesting that direct electron transfer between electrodes may be a possibility.Recently  E. coli . This ab+ in the cytoplasm through the quinone pool. They are based on pathways used by bacteria such as Acidithiobacillus ferrooxidans, whose outer membrane has cytochromes that are responsible for oxidizing iron ore [E. coli is known to interact with an electrode when neutral red serves as a mediator, and this interaction has been demonstrated to affect its cellular metabolism [E. coli has shown dissimilatory iron reduction under cymA expression from Shewanella oneidensis [To account for the interactions with an electrode, two reactions were added to the model reconstruction. These reactions are described in Additional file iron ore -29. Figutabolism . Furthereidensis .G. sulfurreducens at 1.2 μmol/mgProtein-min or 1.07 A/gDW [2. Xie et al showed that E. coli is capable of becoming redox active in the presence of an electrode modulated by a computerized potentiostat [ca. 600 mA/gDW was calculated. The electron uptake rate used in this study (ca. 800 mA/gDW) is consistent with these reported values.Figure 07 A/gDW . The samntiostat . Based oE. coli [The calculation of growth rates and product flux was computationally determined using a Flux Balance Analysis (FBA) framework. The FBA framework has been previously used to analyze growth of E. coli ,34. CompE. coli .E. coli. These heterologous pathways had been previously identified in literature and were incorporated into the model [In addition to the electron transfer reactions described earlier, a number of other modifications were made to the model to account for growth-coupled product flux and the production of biochemicals via pathways not native to he model -40. A suUnless specifically noted, all simulations were performed under anaerobic conditions. A substrate uptake rate of 10 mmol/gDW-hr for glucose was used. For ease of comparison to other substrates on an equal carbon basis, the following values were used for other substrates: 10 mmol/gDW-hr for gluconate and sorbitol. 12 mmol/gDW-hr for xylose; 20 mmol/gDW-hr for glycerol and 5 mmol/gDW-hr for maltose. The electron uptake rate was limited to a maximum of 30 mmol/gDW-hr. The model was allowed to choose any uptake rate that maximized the product flux (or biomass) between 0 and 30 mmol/gDW-hr.E. coli is capable of growing on a number of different carbon sources. The carbon source utilized by the organism can impact the distribution of the fermentation products during anaerobic growth. The change in the by-product secretion patterns, and the corresponding yield of a product, is often associated with the degree of reduction (defined below) of the substrate and the external redox state. Electrical enhancement introduces another perturbation to the cell's redox state. Therefore, to characterize the general impact of electrical enhancement on product yield, we considered various substrate-product pairs, and modelled growth under a variety of different carbon substrates. Many of these product compounds have been identified as commercially valuable by the US Department of Energy [f Energy . Table 1degree of reduction of the substrate and product. We defined the degree of reduction as the number of reducing equivalents produced by a substrate during its oxidation to pyruvate or the number of reducing equivalents consumed to produce a desired metabolite from pyruvate, divided by the carbon length of that substrate or product. These values are shown in Table To establish a systematic method to analyze the relationship between the substrate-product coupling, we compared the yield improvements as a function of the n-butanol allowed us to test the effect of keeping the degree of reduction constant while varying the carbon length and pathway dependency, while products such as succinate, 1,3-propanediol and 1,4-butanediol allowed us to evaluate how different degrees of reduction impact yield. Figure E. coli that consume or produce these substrates and products.The products specified in Table Characterizing changes to ATP yield and biomass growth rate are fundamental to understanding how electrical enhancement impacts cellular metabolism. To determine the extent to which electrical enhancement can influence ATP production, we calculated the maximal ATP yield that could be generated in the presence of electrical enhancement. While the maximization of ATP (without biomass) is not perhaps a physiologically valid objective function, it serves two purposes in understanding how bioelectrosynthesis influences the energetics of the underlying metabolic network. Firstly, it helps to isolate and identify the energy producing pathways that could make the largest contribution to additional ATP for the cell. Secondly, comparison of relative increases in maximal ATP production against increases in biomass yield can help distinguish whether the biomass production is limited by ATP, redox, or carbon availability. By analyzing the differences in ATP and biomass yields, it is possible to identify the constraints on the metabolic network for increasing ATP and biomass yields by bioelectrosynthesis. The results of the simulations are shown in Figure absolute increase in the ATP produced was identical, 1.9 mmol/gDW-hr in all cases except for sorbitol and glycerol utilizing conditions. These two substrates are exceptions because they are already highly reduced and the extra electrons supplied during bioelectrosynthesis have a lower or no benefit on ATP production. The results suggest two important points. The increase in ATP produced by the cell during electrical enhancement is directly proportional to the current supplied to the cell. Secondly, the mechanism by which ATP is produced is linked to specific metabolic pathways that are described in detail in the following section.Figure Since there are a limited number of reactions in the central metabolism that are capable of generating ATP under anaerobic conditions, (acetate kinase (ACKr), pyruvate kinase (PYK), phosphoglycerate kinase (PGK), ATP synthase (ATPs4rpp)), analysis of these four pathways can suggest a mechanism by which electrical enhancement improves ATP yields Figure .E. coli operates ATP synthase as a low flux ATP driven proton pump under anaerobic conditions [Physiologically, nditions . This puin silico to further test this idea.This analysis seems to indicate that energy yields within the cell could be manipulated by controlling the current of the electrode, since an increased flux through the NADH electrode generation reaction could facilitate the generation of a proton gradient. The ability to increase ATP yields within a cell offers new perspectives on how strains could be designed to maximize growth rates, particularly under fermentative conditions when cells are usually energy starved. We examined the ATP production when the objective function was assumed to be maximization of growth rates E. coli under anaerobic conditions (biomass objective) was calculated and plotted as a percentage of the base case . Figure The change in the net ATP flux produced by the four pathways (kinases and the ATP synthase) available to and ATP that leads to higher growth yields. This result suggests that relative increases in biomass yields are greater for oxidized substrates than for more reduced ones.Figures A. succinogenes was able to drive proton translocation and increase ATP synthesis [The above conclusion has the following implications for industrial bioelectrosynthesis. In the presence of an NADH drain on the system, such as biosynthetic pathways, or pathways that might be required for the production of highly reduced metabolites, bioelectrosynthesis offers the possibility of boosting biomass yields by supplying reducing power as well as producing a proton motive force that could generate ATP. This concept is particularly important for strain designs that result in poor growth rates. These strains are usually not industrially viable because of their poor growth rates, but could be made viable by electrically boosting growth rates while maintaining specific knockout strategies geared towards metabolite production. Interestingly, though perhaps by a different mechanism, Park and Zeikus found that electrical enhancement with neutral red as an electron mediator for ynthesis . Their eE. coli shows increases in flux through parts of the pentose phosphate pathway, mid glycolysis and the branched TCA cycle. Changes to the flux of the reactions that belong to these pathways are generally similar and are approximately 10% of the base case. Significant changes in flux appear in those reactions that are capable of consuming NADH. These changes include: 1) the pyruvate metabolism for which ethanol producing pathways have higher fluxes  the acetate producing pathways which have a lower flux value (90% decrease); and 3) ATP synthase shows a change in directionality (for reasons previously described). A metabolic map describing the changes to fluxes in the central metabolism is provided in the Additional file Analysis of the flux distributions of the central metabolism provide insight into how these improvements can be achieved. The wild-type metabolic network of 2, it may be possible to substantially improve yields [Bioelectrosynthesis provides reducing power and if used in conjunction with highly oxidized carbon sources such COe yields . We exple yields  , we wanted to evaluate the potential for bioelectrosynthesis when CO2 is the sole carbon source. The maximum theoretical succinate flux was used as the criteria for evaluating this potential. The maximum theoretical succinate production was calculated to be 0.38 mmol/gDW-hr at a maximum electron uptake rate of 30 mmol/gDW-hr. The calculated succinate flux is very low and this suggests that much larger electron uptake rates are required to achieve product fluxes that are comparable to succinate production from glucose.The growth yield shows the total biomass produced relative to total incoming carbon is larger with electrical enhancement than without. This increase occurs due to a combination of additional CO2 with other hexose and pentose sugars. Our data suggests the microbes capable of reducing CO2 to a desired metabolite such as succinate would require an electron uptake flux an order of magnitude larger than what we used in the model. The equivalent electron flux for substituting glucose would be 2.4 × 102 mmol/gDW-hr for 10 mmol/gDW-hr of glucose. This flux corresponds to current density of 6.8 A/m2 assuming biofilm cell densities reported in the literature [2 [2 as the sole carbon source.The results seem to suggest that the best strategy, in the short term, for synthesizing chemicals or fuels would be the co-utilization of COterature . In someature [2 . This poature [2 . The resProduct yield improvements due to electrical enhancement are dependent on the biochemical compound produced and the substrate utilized. We computed the maximum theoretical product flux for each biochemical compound of interest from each of the substrates. The maximum product yield occurs under conditions of no biomass synthesis and can be computationally determined by solving the stoichiometric model and maximizing the product exchange flux rather than the biomass growth reaction. Maximizing for product synthesis redirects carbon flux and therefore electron transfer to product formation rather than biomass synthesis and establishes the upper limit on the product yield.Changes to the theoretical maximum product flux under electrical enhancement provides insight into the relationship between the substrate and the product, and helps to identify the substrate product electron equivalence quotient (SPEEQ) values for which electrical enhancement is most useful.There are two discernible conclusions in Figure 2 reducing pathways present in the E. coli metabolism. Phosphoenolpyruvate carboxylase (ppc) is the enzyme responsible for this function. Consequently, the additional NADH generated by reactions at the electrode induces the cell to shift its metabolism towards pathways that balance the redox allowing the incorporation of carbon dioxide. This results in a substantial improvement in yield. Hence, this provides an explanation for the counter intuitive observation that large improvements are possible even when SPEEQ is high. It should be noted however, recently, yield improvements may be possible for high SPEEQ conditions when an electrode serves as an electrode sink rather than an electron donor. Flynn et al. were able to show that Shewanella oneidensis could be used to ferment glycerol to ethanol in the presence of an electrode based electron acceptor [While these results are along expected lines, there are a few data points that cluster above the trending line (circled), which suggest large improvements in yield are possible even when the substrate product electron equivalence quotient (SPEEQ) is relatively high. Analysis shows that these points are associated with the production of succinate and 1,4-butanediol, and can be attributed to the COacceptor .ppc activity has on yield improvement. For succinate production, the flux through this reaction can be increased by as much as 14-44% for optimum production under electrically enhanced conditions. The relative increase in 1,4-butanediol production is even greater when the ppc activity is present. The predicted flux of n-butanol is also increased under electrically enhanced conditions, however this is attributable largely to the additional reducing power available to drive the reactions forward. The flux through ppc is relatively low for n-butanol synthesis compared to other products since acetyl-CoA is the precursor metabolite and not oxaloacetate. Fluxes for a few key reactions are shown in Table A closer look at the output from the simulations reveals the importance that ppc has resulted in higher succinate production under anaerobic conditions [ppc as a response to NADH generation. Increasing the applied current to the cells would result in increased NADH which would subsequently change the cell's NADH/NAD+ ratio. Those pathways that are capable of regenerating NAD+ would in turn see higher fluxes. When these pathways incorporate CO2, the product yields are relatively higher. This result suggests that as other pathways of carbon fixation that regenerate NAD+ are incorporated into the metabolism, their fluxes may be controlled by directly influencing the NADH generation rate through an electrode.While studies have shown that overexpression of nditions  this datn-butanol are examples of these and they exhibit similar enhancement. Additionally substrates with the same degree of reduction  show almost identical levels of enhancement once again suggesting the importance that the SPEEQ has on product yield improvement.Theoretical improvements in yield are notably similar for products that have the same degree of reduction but are metabolically many reaction steps apart. Ethanol and Under growth coupled product formation, electrical enhancement can impact the metabolism in a number of ways. We evaluated two scenarios where electrical enhancement could significantly impact the cell, and therefore, the growth coupled product flux. We examined the impact of electrical enhancement on 1) growth coupled strategies for various substrate-product couplings to show that electrical enhancement can be used on under growth coupled scenarios  than yield with respect to substrate uptake rate in most cases. An exception was succinate production under electrically enhanced conditions, because the carbon fixing reaction, ppc, is part of the succinate producing pathway. The succinate yield relative to total incoming carbon is 21% and 16% for glucose and xylose, respectively. While this is lower than the yield relative to only glucose or xylose, bioelectrosynthesis still improves the overall efficiency of product synthesis since a greater proportion of total incoming carbon ends up in the product.The yield calculations in Figure + must be regenerated by reducing partially oxidized metabolic intermediates such as pyruvate to lactate or ethanol (etc.) that are then excreted from the cell. The amount of each intermediate can be modulated by the cell to balance the reducing equivalents consumed and produced during fermentation so that it can grow on a variety of different substrates. However, there exists a balance between managing the redox balance within the cell  and maximizing energy yields through other pathways . Product formation is often coupled to growth via knock-outs that perturb these NAD+ regenerating pathways, forcing carbon flux to be directed towards desired products. Electrical enhancement introduces another redox disturbance to the cell, to direct additional carbon through these pathways. This perturbation can often come at the expense of growth rate.During biomass growth, NADTo understand this better, we examined the production envelopes for individual biochemicals under electrically and non-electrical enhanced conditions with glucose as the substrate. All four graphs in Figure n-butanol production  supplied (generated) by an electrode can, but does not necessarily appear in the desired product. For example, in the case of n Figure , electri-1. By comparison, forcing electrons at 30 mmol/gDW-hr reduces growth rate to 0.058 hr-1 and increases product flux to 21.6 mmol/gDW-hr. This result suggests that the best strategy for electrically enhancing a bioprocess would be a dynamic one. Having no electrical enhancement during the beginning of a batch would maximize growth rate, and once a threshold cell density is reached, growth rate can be sacrificed for improved product yield with electrical enhancement. However, additional optimization will be required to identify the optimal time of switching from a growth phase to a production phase [This result shows the inherent trade-off that can exist between the improved yield and the growth rate of the organism, but that it appears to be conditional on the active metabolic pathways. Consider further that the optimum electron uptake rate predicted computationally for ethanol production is 20.6 mmol/gDW-hr with a predicted growth rate of 0.075 hron phase . The impUnder growth coupled product synthesis, fewer reactions are available for the cell to respond to electrically induced redox changes. Inspite of the changes in the metabolism, the network still has to balance the additional reducing equivalents supplied from the electrode. Therefore, the specific pathways or the magnitude of that response may be different from the wild type. We analysed the changes in the flux distribution for the four scenarios in Section 3.4 and a summary of the results are provided in Table The trade-off between product flux and growth rate is capable of significantly affecting the overall productivity of a system. We examined three specific knock-out strategies for ethanol production to characterize the effect of electrical enhancement on strain design and its productivity.et al. [The production envelopes for three different strategies are shown in Figure et al. . The figIn addition, since electrical enhancement is essentially current supplied at an electrode, by varying the current, it is possible to operate along a particular section of the production envelope. A number of these operating points represented by blue dots are shown for the 3 knockout strategy. Depending on the magnitude of this current, it is possible to operate along the maximum growth rate, or at some other point that improves the product flux while lowering growth rate. This flexibility has significant consequences for implementing a dynamic strategy that maximizes productivity. Such a strategy would use no current (or low current) early during a batch to maximize biomass and then increase the current to maximize product flux.Traditional strategies that attempt to manipulate NADH availability, namely, the utilization of carbon source with different redox states and the genetic manipulation of the host cell are generally well understood. Bioelectrosynthesis is a third method that has been used; however a systematic understanding of the potential of this technique hasn't been fully evaluated. We have provided a systematic understanding of how electrical enhancement can impact cellular metabolism.E. coli, shows that electrical generation of reducing equivalents is capable of influencing the three most important aspects of bioprocesses: biochemical yield, cellular ATP yield and cellular growth rate. Understanding these processes has provided insight into the long term strategies for electrical enhancement techniques and we have highlighted the significance of these findings both for the application of electrical enhancement at an industrial scale as well as for rational strain design.Our evaluation of electrical enhancement on a complete genome scale network of the model organism, E. coli we were able to increase the activity of the phosphoenolpyruvate carboxylase pathway because the increased demand to regenerate NAD+ required the use of this pathway. Since there is significant interest to use bioelectrosynthesis on carbon dioxide only, we extended this concept to compare the difference between sole CO2 utilization and co-utilization of CO2 and hexose. Our results suggest that while bioelectrosynthesis is possible on CO2 alone, its very low yield probably make it unsuitable at an industrial scale unless significant advances are made in understanding and optimizing electron transfer rates from an electrode. A constraint on the upper electron uptake limit means that in the short term at least, electrical enhancement strategies that co-utilize CO2 and sugar substrates are more feasible.Our results showed that manipulating cellular redox conditions can force the metabolism to increase the utilization of pathways that fix carbon dioxide. For The extent to which electrical enhancement is capable of influencing ATP and biomass yields provided a basis for understanding the implications that bioelectrosynthesis has on strain design. Our results suggest that electrical enhancement is generally compatible with existing metabolic engineering strategies, although sometimes it may be necessary to recalculate specific strategies under electrically enhanced conditions. The potential trade-off between the biomass growth rate and product yields suggests that there is room for process optimization during bioelectrosynthesis where the organism may grow under non-electrically conditions initially and then electrically enhanced conditions once certain growth conditions have been met.Shewanella, Geobacter or Acidithiobacillus to improve electron transfer rates would be the next logical step. Our results suggest that influencing the NADH/NAD+ ratio is possible with an electrode - and in particular that this may form the basis for controlling fluxes through some reactions, notably those that oxidize NADH and alleviate redox constraints. Further work needs to examine the network level adaptations of an organism in response to these redox perturbations using metabolic flux analysis with 13C isotope labelled substrates and the scale-up of these systems, in much the same way that fuel cells are being considered for scale up.Finally additional work needs to be done to understand the microbiology and physiology of electricigens and their ability to accept electrons. There are a number of possible limitations that can arise, and in particular incorporation of components of the electron transport chain from organisms such as This computational study lays the framework for understanding where electrical enhancement is most useful and evaluating potential benefits as well as limitations. The concept of electrical enhancement is promising for products that are highly reduced. These concepts could be beneficial in developing strategies for chemicals or fuels  that require large amounts of reducing power via NADH. Yields of large molecules such as those that would be required to make fuels could be improved by electrical enhancement.The experimental validation of the principles described herein is critical to further understand and optimize microbial bioelectrosynthesis of key biochemical products and develop economically viable and environmentally friendly commercial bioprocesses.The authors declare that they have no competing interests.A.V. P. discussed the study, developed the model, performed simulations, interpreted data and wrote the paper. R.M. conceived the study, interpreted the data and wrote the paper. All authors read and approved the final manuscript.Model Reactions/Deletion Strategies.Click here for file2 Incorporating Pathways, Maximizing ATPFlux Profiles - Maximizing Biomass; CO.Click here for fileMetabolic Maps of Changes in Flux Distributions.Click here for fileMaximizing Product Flux Sample of Glucose Flux Profile.Click here for fileChanges in Flux Distribution; Yield Data Based on Total Incoming Carbon.Click here for file


Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments.Correlation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer.Metabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics. Within systems biology, metabolomic studies have been shown to be an essential component for elucidating the interdependency between substrates and products. To date, metabolomics data have been limited to relatively standard statistical techniques including univariate analyses and multivariate analyses such as principal component analysis ,2. Whilset al. recently demonstrated the application of differential correlation analysis to create networks offering an insight into characterising a variety of biological states [The concentrations of metabolites in cells relate in part to the underlying structure of the metabolic network and thus the relative concentrations of metabolites within these networks can exhibit correlations . There hl states . Whilst l states  and the l states  as well l states . The EHMl states . Dependil states .Escherichia coli[In this study a network-based correlation analysis method has been developed as a novel method for the identification of linked metabolic pathways in the context of the entire metabolic network. Pair-wise correlations were mapped onto the EHMN genome-scale metabolic network and the shortest path connecting the two metabolites was extracted and used to construct a network of the cellular response to a physiological perturbation. Since all reactions present in the EHMN are reversible, all correlations were considered reversible and in this way it does not matter in which direction the path was drawn between correlated metabolites. Network biology is an emerging field; however there are complex issues, which remain to be addressed. For example, the shortest path between metabolites has been previously described for chia coli. The avechia coli.in vitro cell models [via. cell-environment interaction). Rather than simply to analyse the data in terms of each single metabolic feature that changes with respect to oxygen level, we have applied our novel correlation mapping onto the EHMN to reveal both correlated metabolites whose concentrations change in unison and anti-correlated discordant metabolites.We shall illustrate this method for investigating tumour hypoxia. Cancer metabolism has fascinated biologists since Warburg’s experiments in the 1920s . Otto Wal models ,17. Herein vitro and exposed to 21% oxygen (normoxia) or 1% oxygen (hypoxia) have been compared to assess the metabolic effect of lowering oxygen availability and to determine whether or not these effects are commonly observed across different cell lines. The cell lines used were the human breast carcinoma cell line MDA-MB-231 and the human colon carcinoma cell line HCT116. Cell lysates were prepared for each cell line exposed to each experimental oxygen condition and metabolites were profiled using gas chromatography–mass spectrometry (GC-MS). For each cell line exposed to each condition, a total of 30 replicates were collected. Over 50 metabolites were detected using GC-MS in each cell line exposed to normoxia or hypoxia  cycle though glutaminolysis. The TCA cycle is part of central carbon metabolism and it is widely known to be one of the main responses to hypoxia . Glutamiy attack ,23. The Although it has not previously been linked with hypoxia, the greatest difference in correlation between HCT116 cells exposed to normoxia and hypoxia occurred in the correlation between galactose/glucose and 4-hydroxyproline. These metabolites are correlated in hypoxia but are not correlated in normoxia and must therefore be a response to hypoxia. Furthermore, the 4-hydroxyproline peak was correlated with many other metabolites in hypoxia and could be potentially considered a central 'hub’ in cellular response to this oxygen condition, such that pathways involving this metabolite are changed in regulation. This highlights the potential importance of this metabolite in a hypoxic response.Many of the metabolites found to be differently correlated between normoxia and hypoxia in both cell lines were features of central carbon metabolism. Such metabolites include glucose, malate and pyruvate. Central carbon metabolism is obviously a feature of hypoxic response that is conserved between cell lines; however, the way the concentrations of these metabolites is affected by hypoxia is to a certain extent cell line specific. Other central carbon metabolites may also have changed in the same way within the system, just that they were not detected. It is possible to measure all central metabolites and they are present in our libraries, however many can be missing from the profile if their concentrations are below the limits of detection .2O and NAD. When computing the shortest network path connecting two nodes the path calculates a shortcut that passes through these highly connected nodes [The metabolic profiles acquired for correlation analysis here were obtained from whole cell lysates of MDA-MB-231 and HCT116 human carcinoma cells. For this reason it was appropriate to use a metabolic reconstruction that did not contain compartmentalisation of metabolites, for example those biochemical reactions that occur in the cytosol and those that are contained in the mitochondria. The EHMN contains approximately 2800 non-compartmentalised reactions . Correlaed nodes . These mvia the shortest route. This means many pathways form interactions between traditional pathways and thus cross over them  supplemented with 10% fetal calf serum  from a single batch and 2 mM glutamine . Cells were incubated in 95% air and 5% CO6 cells/mL into a 10 cm2 Petri dish . For consistency, the same batch of undefined FCS was used for all experiments. Cells were cultured and allowed to adhere in normoxia (21% oxygen) for 24 h prior to experimental analysis in the incubator described above. Subsequently, cells were divided into two groups where one group was retained in the normoxia condition and the other group was placed in a closed vessel though which gas containing 1% O2, 5% CO2 balanced with N2 was flowed at a rate 2 mL/s (hypoxia). This vessel was developed in-house and was constructed from an ADDIS® 5 L air tight container (including removable clip lid) with a male BSPT- female BSPP reducer and male parallel straight adaptor to connect a 6 mm polytube to a 6 mm 2/2 finger tap at either side of the container .8 mL of cells were seeded, in exponential phase, at 1 × 10g for 15 min) and the supernatant was transferred into a fresh Eppendorf tube. The volume of supernatant to be lyophilised was normalised according to the weight of the pellet for intracellular samples. Furthermore, supernatant remaining after normalisation was used to generate QCs which contained an equal proportion from each sample. Supernatant for both the sample and QC were lyophilised.Prior to metabolite extraction, the hypoxic vessel was closed using the taps and placed in an anoxic chamber . Normoxic samples were extracted in air. Extracellular media were decanted and cells were washed three times with 1 mL phosphate buffered saline (PBS). Subsequently, 1 mL methanol (maintained at -48°C) was added to quench cellular metabolism. Cells were scraped from the culture surface and the suspension solution was placed into Eppendorf tubes. A series of three freeze thaw cycles using liquid nitrogen were performed to enhance metabolite extraction before which the solution was centrifuged  to each sample which was then vortexed and heated at 60°C for 30 min. Finally, samples were centrifuged at 17000 × Samples were analysed using an Agilent 6890 GC  coupled to a LECO Pegasus III  EI-ToF-MS. The GC-MS instrument setup used has been previously described ,28. The The processing of raw GC-MS data was performed following the methods described previously, using the LECO ChromaToF v3.25 software package to apply the chromatographic deconvolution algorithm . Identifd4. QC metabolites with a CV greater than 30% were removed from the whole data matrix [Data were normalised to the internal standard succinic acid a matrix . Prior ta matrix . These dThe Pearson’s correlation calculation is given in Equation 1:r is the correlation coefficient calculated for the pair-wise correlation of variables X and Y, X and Y respectively and n is the number of samples.Equation 1: The Pearson’s product–moment correlation equation (as shown in Rodgers and Nicewander (1988) ) where rTo test for a significant correlation, the correlation value was approximately normalised using the inverse transform of Equation 2 which is known as the Fishers C is the correlation coefficient , which approximately normalises the distribution of the correlation coefficient to a Gaussian distribution and is independent on the number of samples.Equation 2: Fishers The significance of the correlation was tested using Equation 3.N), where the distribution is approximately Gaussian and the 95% significance level is given by Equation 3: Determining the significance of the correlation that is dependent on sample number  these correlations were discarded.Since the correlation threshold was relatively low for SE is the standard error, ρ is the correlation coefficient and n is the sample size.Equation 5: The standard error equation where α = 0.05 was tested using Equation 6 to satisfy the For each metabolite detected, the GC peak area was used for correlation. The Pearson’s correlation coefficients between metabolites were computed pair-wise using Equation 1. Subsequently, the difference between the correlations for two sample types  was calculated. To be considered a significant difference, one of the correlations must be > 0.7 and the difference between the correlations coefficients must be > 0.407. This was determined using the Fishers Ci is the Pearson’s rank correlation coefficient for example, 0.7. (b) Permutation test for comparing correlations between metabolites, where z1 and z2 are the values calculated through equation 3a and N1 and N2 are the minimum sample sizes for each metabolite [Equation 6: (a) Fishers tabolite .2O, CO2, O2, orthophosphate and hydrogen [Pair-wise correlations that significantly differed with respect to sample treatment were mapped directly onto the uncompartmentalised EHMN. For this the pair-wise correlations were taken in turn and using graph theory the shortest pathway connecting these two metabolites was computed. This was done using in-house routines in Matlab. The network was imported into Matlab using the SBML toolbox . Subsequhydrogen ,35.A stoichiometric matrix of the metabolic network was constructed using the SBML toolbox. This matrix was used to extract the two separate matrices of metabolites consumed and produced. The consumed network was multiplied by the transpose of the produced to construct a metabolite connectivity network. The matrix was symmetrised to account for reversibility in the network. Graph theory was implemented to calculate the shortest pathway between two metabolites using the bioinformatics toolbox included in Matlab.The authors declare that they have no competing interests.HK and EA carried out data acquisition, data analysis and wrote the manuscript. JA and WB assisted in the data acquisition. KS helped in the initial data analysis. RG and KW participated in the design, data interpretation and reviewing of manuscript. All authors read and approved the final manuscript.GC-MS data after normalisation to internal standard and filtering based on QCs.Click here for file