Do genome‐scale models need exact solvers or clearer standards?

Abstract

Correspondence14 October 2015Open Access Do genome-scale models need exact solvers or clearer standards? Ali Ebrahim Ali Ebrahim [email protected] Department of Bioengineering, University of California, San Diego, CA, USA Search for more papers by this author Eivind Almaas Eivind Almaas Department of Biotechnology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway Search for more papers by this author Eugen Bauer Eugen Bauer Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg Search for more papers by this author Aarash Bordbar Aarash Bordbar Sinopia Biosciences Inc., San Diego, CA, USA Search for more papers by this author Anthony P Burgard Anthony P Burgard Genomatica, Inc., San Diego, CA, USA Search for more papers by this author Roger L Chang Roger L Chang Department of Systems Biology, Harvard Medical School, Boston, MA, USA Search for more papers by this author Andreas Dräger Andreas Dräger Department of Bioengineering, University of California, San Diego, CA, USA Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany Search for more papers by this author Iman Famili Iman Famili Intrexon, Inc., San Diego, CA, USA Search for more papers by this author Adam M Feist Adam M Feist Department of Bioengineering, University of California, San Diego, CA, USA Search for more papers by this author Ronan MT Fleming Ronan MT Fleming Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg Search for more papers by this author Stephen S Fong Stephen S Fong Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA Search for more papers by this author Vassily Hatzimanikatis Vassily Hatzimanikatis Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Search for more papers by this author Markus J Herrgård Markus J Herrgård The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Search for more papers by this author Allen Holder Allen Holder Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA Search for more papers by this author Michael Hucka Michael Hucka Department of Computing and Mathematical Science, California Institute of Technology, Pasadena, CA, USA Search for more papers by this author Daniel Hyduke Daniel Hyduke Department of Biological Engineering, Utah State University, Logan, UT, USA Search for more papers by this author Neema Jamshidi Neema Jamshidi Department of Radiology, University of California, Los Angeles, CA, USA Institute of Engineering in Medicine, University of California, San Diego, CA, USA Search for more papers by this author Sang Yup Lee Sang Yup Lee The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea Search for more papers by this author Nicolas Le Novère Nicolas Le Novère Babraham Institute, Cambridge, UK Search for more papers by this author Joshua A Lerman Joshua A Lerman Department of Bioengineering, University of California, San Diego, CA, USA Search for more papers by this author Nathan E Lewis Nathan E Lewis Department of Pediatrics, University of California, San Diego, CA, USA Search for more papers by this author Ding Ma Ding Ma Department of Management Science and Engineering, Stanford University, Stanford, CA, USA Search for more papers by this author Radhakrishnan Mahadevan Radhakrishnan Mahadevan Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada Search for more papers by this author Costas Maranas Costas Maranas Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA Search for more papers by this author Harish Nagarajan Harish Nagarajan Genomatica, Inc., San Diego, CA, USA Search for more papers by this author Ali Navid Ali Navid Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, USA Search for more papers by this author Jens Nielsen Jens Nielsen The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Search for more papers by this author Lars K Nielsen Lars K Nielsen Australian Institute for Bioengineering & Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland, Australia Search for more papers by this author Juan Nogales Juan Nogales Department of Environmental Biology, Centro de Investigaciones Biológicas (CSIC), Madrid, Spain Search for more papers by this author Alberto Noronha Alberto Noronha Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg Search for more papers by this author Csaba Pal Csaba Pal Synthetic and Systems Biology Unit, Biological Research Center, Szeged, Hungary Search for more papers by this author Bernhard O Palsson Bernhard O Palsson Department of Bioengineering, University of California, San Diego, CA, USA Search for more papers by this author Jason A Papin Jason A Papin Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA Search for more papers by this author Kiran R Patil Kiran R Patil European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Nathan D Price Nathan D Price Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Jennifer L Reed Jennifer L Reed Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Michael Saunders Michael Saunders Department of Management Science and Engineering, Stanford University, Stanford, CA, USA Search for more papers by this author Ryan S Senger Ryan S Senger Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA Search for more papers by this author Nikolaus Sonnenschein Nikolaus Sonnenschein The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Search for more papers by this author Yuekai Sun Yuekai Sun Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA Search for more papers by this author Ines Thiele Ines Thiele Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg Search for more papers by this author Ali Ebrahim Ali Ebrahim [email protected] Department of Bioengineering, University of California, San Diego, CA, USA Search for more papers by this author Eivind Almaas Eivind Almaas Department of Biotechnology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway Search for more papers by this author Eugen Bauer Eugen Bauer Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg Search for more papers by this author Aarash Bordbar Aarash Bordbar Sinopia Biosciences Inc., San Diego, CA, USA Search for more papers by this author Anthony P Burgard Anthony P Burgard Genomatica, Inc., San Diego, CA, USA Search for more papers by this author Roger L Chang Roger L Chang Department of Systems Biology, Harvard Medical School, Boston, MA, USA Search for more papers by this author Andreas Dräger Andreas Dräger Department of Bioengineering, University of California, San Diego, CA, USA Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany Search for more papers by this author Iman Famili Iman Famili Intrexon, Inc., San Diego, CA, USA Search for more papers by this author Adam M Feist Adam M Feist Department of Bioengineering, University of California, San Diego, CA, USA Search for more papers by this author Ronan MT Fleming Ronan MT Fleming Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg Search for more papers by this author Stephen S Fong Stephen S Fong Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA Search for more papers by this author Vassily Hatzimanikatis Vassily Hatzimanikatis Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Search for more papers by this author Markus J Herrgård Markus J Herrgård The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Search for more papers by this author Allen Holder Allen Holder Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA Search for more papers by this author Michael Hucka Michael Hucka Department of Computing and Mathematical Science, California Institute of Technology, Pasadena, CA, USA Search for more papers by this author Daniel Hyduke Daniel Hyduke Department of Biological Engineering, Utah State University, Logan, UT, USA Search for more papers by this author Neema Jamshidi Neema Jamshidi Department of Radiology, University of California, Los Angeles, CA, USA Institute of Engineering in Medicine, University of California, San Diego, CA, USA Search for more papers by this author Sang Yup Lee Sang Yup Lee The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea Search for more papers by this author Nicolas Le Novère Nicolas Le Novère Babraham Institute, Cambridge, UK Search for more papers by this author Joshua A Lerman Joshua A Lerman Department of Bioengineering, University of California, San Diego, CA, USA Search for more papers by this author Nathan E Lewis Nathan E Lewis Department of Pediatrics, University of California, San Diego, CA, USA Search for more papers by this author Ding Ma Ding Ma Department of Management Science and Engineering, Stanford University, Stanford, CA, USA Search for more papers by this author Radhakrishnan Mahadevan Radhakrishnan Mahadevan Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada Search for more papers by this author Costas Maranas Costas Maranas Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA Search for more papers by this author Harish Nagarajan Harish Nagarajan Genomatica, Inc., San Diego, CA, USA Search for more papers by this author Ali Navid Ali Navid Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, USA Search for more papers by this author Jens Nielsen Jens Nielsen The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden Search for more papers by this author Lars K Nielsen Lars K Nielsen Australian Institute for Bioengineering & Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland, Australia Search for more papers by this author Juan Nogales Juan Nogales Department of Environmental Biology, Centro de Investigaciones Biológicas (CSIC), Madrid, Spain Search for more papers by this author Alberto Noronha Alberto Noronha Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg Search for more papers by this author Csaba Pal Csaba Pal Synthetic and Systems Biology Unit, Biological Research Center, Szeged, Hungary Search for more papers by this author Bernhard O Palsson Bernhard O Palsson Department of Bioengineering, University of California, San Diego, CA, USA Search for more papers by this author Jason A Papin Jason A Papin Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA Search for more papers by this author Kiran R Patil Kiran R Patil European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Nathan D Price Nathan D Price Institute for Systems Biology, Seattle, WA, USA Search for more papers by this author Jennifer L Reed Jennifer L Reed Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA Search for more papers by this author Michael Saunders Michael Saunders Department of Management Science and Engineering, Stanford University, Stanford, CA, USA Search for more papers by this author Ryan S Senger Ryan S Senger Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA Search for more papers by this author Nikolaus Sonnenschein Nikolaus Sonnenschein The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Search for more papers by this author Yuekai Sun Yuekai Sun Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA Search for more papers by this author Ines Thiele Ines Thiele Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg Search for more papers by this author Author Information Ali Ebrahim1, Eivind Almaas2, Eugen Bauer3, Aarash Bordbar4, Anthony P Burgard5, Roger L Chang6, Andreas Dräger1,7, Iman Famili8, Adam M Feist1, Ronan MT Fleming3, Stephen S Fong9, Vassily Hatzimanikatis10, Markus J Herrgård11, Allen Holder12, Michael Hucka13, Daniel Hyduke14, Neema Jamshidi15,16, Sang Yup Lee11,17, Nicolas Le Novère18, Joshua A Lerman1, Nathan E Lewis19, Ding Ma20, Radhakrishnan Mahadevan21, Costas Maranas22, Harish Nagarajan5, Ali Navid23, Jens Nielsen11,24, Lars K Nielsen25, Juan Nogales26, Alberto Noronha3, Csaba Pal27, Bernhard O Palsson1, Jason A Papin28, Kiran R Patil29, Nathan D Price30, Jennifer L Reed31, Michael Saunders20, Ryan S Senger32, Nikolaus Sonnenschein11, Yuekai Sun33 and Ines Thiele3 1Department of Bioengineering, University of California, San Diego, CA, USA 2Department of Biotechnology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway 3Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg 4Sinopia Biosciences Inc., San Diego, CA, USA 5Genomatica, Inc., San Diego, CA, USA 6Department of Systems Biology, Harvard Medical School, Boston, MA, USA 7Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany 8Intrexon, Inc., San Diego, CA, USA 9Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA 10Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland 11The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark 12Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA 13Department of Computing and Mathematical Science, California Institute of Technology, Pasadena, CA, USA 14Department of Biological Engineering, Utah State University, Logan, UT, USA 15Department of Radiology, University of California, Los Angeles, CA, USA 16Institute of Engineering in Medicine, University of California, San Diego, CA, USA 17Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea 18Babraham Institute, Cambridge, UK 19Department of Pediatrics, University of California, San Diego, CA, USA 20Department of Management Science and Engineering, Stanford University, Stanford, CA, USA 21Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada 22Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA 23Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, USA 24Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden 25Australian Institute for Bioengineering & Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland, Australia 26Department of Environmental Biology, Centro de Investigaciones Biológicas (CSIC), Madrid, Spain 27Synthetic and Systems Biology Unit, Biological Research Center, Szeged, Hungary 28Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA 29European Molecular Biology Laboratory, Heidelberg, Germany 30Institute for Systems Biology, Seattle, WA, USA 31Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA 32Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA 33Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA Molecular Systems Biology (2015)11:831https://doi.org/10.15252/msb.20156157 Comment on: L Chindelevitch et al (October 2014) See reply: L Chindelevitch et al (in this issue) PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Constraint-based analysis of genome-scale models (GEMs) arose shortly after the first genome sequences became available. As numerous reviews of the field show, this approach and methodology has proven to be successful in studying a wide range of biological phenomena (McCloskey et al, 2013; Bordbar et al, 2014). However, efforts to expand the user base are impeded by hurdles in correctly formulating these problems to obtain numerical solutions. In particular, in a study entitled “An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models” (Chindelevitch et al, 2014), the authors apply an exact solver to 88 genome-scale constraint-based models of metabolism. The authors claim that COBRA calculations (Orth et al, 2010) are inconsistent with their results and that many published and actively used (Lee et al, 2007; McCloskey et al, 2013) genome-scale models do support cellular growth in existing studies only because of numerical errors. They base these broad claims on two observations: (i) three reconstructions (iAF1260, iIT341, and iNJ661) compute feasibly in COBRA, but are infeasible when exact numerical algorithms are used by their software (entitled MONGOOSE); (ii) linear programs generated by MONGOOSE for iIT341 were submitted to the NEOS Server (a Web site that runs linear programs through various solvers) and gave inconsistent results. They further claim that a large percentage of these COBRA models are actually unable to produce biomass flux. Here, we demonstrate that the claims made by Chindelevitch et al (2014) stem from an incorrect parsing of models from files rather than actual problems with numerical error or COBRA computations. Calculating numerically accurate and thermodynamically consistent flux states To prove the feasibility of biomass production in the chosen three models, along with some others, we used the same rational solver QSopt_ex (Applegate et al, 2007) to compute feasible flux states. Moreover, we used SymPy, a symbolic math library (Joyner et al, 2012), to show that the exactly computed feasible flux state has no numerical error. Furthermore, the computed optimal growth rate from QSopt_ex matched those computed by several floating-point solvers accessed via cobrapy (CPLEX, gurobi, glpk, and MOSEK) and the COBRA toolbox (gurobi and CPLEX) to well within a precision of 10−6. Using linear programming problems generated by COBRA for iIT341 and a version of the model we constrained to produce no biomass, we observed consistent results between COBRA and the reputable solvers hosted on the NEOS server. These results unequivocally demonstrate that these COBRA models solve consistently with both rational and floating-point solvers. We were able to extend this analysis to show 23 out of 29 models that Chindelevitch et al (2014) claim to be “blocked” by FBA have solutions that produce biomass flux without numerical error (Table EV1). Thus, the authors' claim that exact arithmetic is necessary for consistency and reproducibility is inaccurate, along with their findings that these previously published and computed models do not produce biomass flux. The authors further claim that even more models are “energy blocked” and cannot produce a feasible flux state to produce biomass without thermodynamically infeasible cycles (often referred to as type III loops). Using loopless FBA (Schellenberger et al, 2011a), we were able to compute solutions that produce biomass without using these loops. Moreover, we demonstrate that in the case that all reactions allow 0 flux (as is the case in the MONGOOSE formulation), all solutions with loops can be converted into solutions without loops and still produce biomass. As these solutions were obtained using an existing algorithm, the inability of MONGOOSE to identify such solutions is a limitation on the method used by MONGOOSE, not on the published reconstructions as stated by Chindelevitch et al (2014). In total, our analysis shows that for 51 out of 59 models, the claims made by MONGOOSE about model blockage are incorrect (Table EV1). A call for clear standards in model formulation While the article by Chindelevitch et al (2014) has a valid goal of computing flux states that have been diligently checked for numerical error and thermodynamically infeasible loops, its general conclusions about the current state of COBRA models are incorrect. While more new tools to ensure model quality are welcome, conventional checks with minimal computational overhead already exist, and are routinely employed by the community of flux balance analysis users to ensure that models produce numerically accurate and thermodynamically consistent flux states. We have identified the primary source of the differences between our computations and those reported by Chindelevitch et al (2014) to be difficulties with parsing reconstructions from published files and their conversion into computable models. Many of the models were read from reconstructions encoded as SBML files. The mechanism of encoding COBRA model information along with a reconstruction in SBML was originally defined by the COBRA toolbox (Schellenberger et al, 2011b), which we therefore consider the reference implementation. For example, as a part of the SBML encoding, boundary metabolites are written with their SBML boundary condition set to true for “exchange” reactions. This convention is meant to signify a system boundary where extracellular metabolites enter and leave the system. The parser developed by Chindelevitch et al (2014) to read models from SBML reconstructions ignores this distinction and therefore adds additional constraints to the model. These incorrectly added constraints block any metabolites from entering the system, causing the models to give infeasible growth solutions consistent with mass balance, because mass is not entering and therefore no growth is possible. Thus, erroneous results and conclusions reported by Chindelevitch et al (2014) resulted from incorrect parsing of SBML files, resulting in ill-formulated models and a misinterpretation of their calculations. Part of the issue, however, rests with difficulties associated with encoding models in a consistent format between different labs and software packages. As is the practice in the field, we contacted the authors of the models that we could not solve in order to resolve the differences; after all, the models had been used to perform COBRA computations in their respective publications. In these cases, the authors were able to supply a “fixed” SBML file after correcting errors in the SBML encoding in their respective codebases. An example of one such error was the presence of both “CO2” and “co2” as metabolites in the SBML file for iVS941 (Satish Kumar et al, 2011). While the GAMS software used in simulating that model is case-insensitive and correctly creates one constraint, parsing the file in other packages (such as the COBRA toolbox, cobrapy, and MONGOOSE) incorrectly created two separate constraints for the uppercase and lowercase versions. Therefore, an inadvertent error in a file-encoding led to different mathematical models in different software tools, and working with the authors of the original model was necessary to resolve the differences. Out of the 88 models attempted by Chindelevitch et al (2014), we were able to solve 80, and 9 of these required modifications to fix encoding errors. We attempted to parse 6 of the remaining 8 reconstructions. While the models we parsed from these reconstructions did not solve, this result was still consistent between floating-point and exact solvers. This situation is a symptom of the well-known issue with interoperability of reconstructions between different laboratories and software packages in constraint-based modeling (Ravikrishnan & Raman, 2015). We believe we can improve upon these issues by better adhering to the standard practices of openness and reproducibility (Dräger & Palsson, 2014). We believe the community needs to standardize on the most recent version of the flux balance constraints (fbc) extension to SBML as the single well-specified format to reliably encode reconstructions, as strict use of fbc version 2 was specifically designed to build genome-scale models unambiguously [SBML-flux Working Group, 2014 SBML Flux Balance Constraints (fbc), http://sbml.org/Documents/Specifications/SBML_Level_3/Packages/Flux_Balance_Constraints_(flux) (Accessed June 13, 2015)]. Therefore, we propose that new reconstructions be published as validated SBML+fbc files and that the authors of existing reconstructions convert them into this format. Moreover, in the interests of reproducibility, studies including flux balance analysis on these genome-scale models should strive to make their code easily reproducible. The models and code used in this study are available as Dataset EV1 and also at https://github.com/opencobra/m_model_collection. Acknowledgements We thank Leonid Chindelevitch for extensive discussions and for sharing results obtained with the MONGOOSE platform for comparison with solutions obtained with COBRA software. Author contributions AE wrote the code and assembled the models included in Dataset EV1. All of the authors contributed to the design, approach, and written manuscript. Subsequent authors are arranged alphabetically by last name. 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BMC Syst Biol 5: 28CrossrefPubMedWeb of Science®Google Scholar Schellenberger J, Lewis NE, Palsson BØ (2011a) Elimination of thermodynamically infeasible loops in steady-state metabolic models. Biophys J 100: 544–553CrossrefCASPubMedWeb of Science®Google Scholar Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson BØ (2011b) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6: 1290–1307CrossrefCASPubMedWeb of Science®Google Scholar Previous ArticleNext Article Read MoreAbout the coverClose modalView large imageVolume 11,Issue 10,October 2015Cover: This month's cover highlights the Research Article "The gut microbiota modulates host amino acid and glutathione metabolism in mice" by Adil Mardinoglu and colleagues. Transcriptomics data of several tissues obtained from conventionally raised and germ-free mice are analyzed using tissue-specific genome-scale metabolic models (GEMs). The study reveals systemic effects of the gut microbiota on host amino acid and glutathione metabolism, and the model predictions are validated by measuring amino acid levels in the hepatic portal vein. © Cover concept by Adil Mardinoglu. (Artistic rendition by Uta Mackensen). Volume 11Issue 101 October 2015In this issue ReferencesRelatedDetailsLoading ...