{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:51:52Z","timestamp":1777632712103,"version":"3.51.4"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100019180","name":"HORIZON EUROPE European Research Council","doi-asserted-by":"publisher","award":["801747"],"award-info":[{"award-number":["801747"]}],"id":[{"id":"10.13039\/100019180","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019180","name":"HORIZON EUROPE European Research Council","doi-asserted-by":"publisher","award":["101000309"],"award-info":[{"award-number":["101000309"]}],"id":[{"id":"10.13039\/100019180","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-024-05651-7","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T05:34:56Z","timestamp":1705988096000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Predicting microbial interactions with approaches based on flux balance analysis: an evaluation"],"prefix":"10.1186","volume":"25","author":[{"given":"Cl\u00e9mence","family":"Joseph","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4405-6802","authenticated-orcid":false,"given":"Haris","family":"Zafeiropoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6211-7042","authenticated-orcid":false,"given":"Kristel","family":"Bernaerts","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7129-2803","authenticated-orcid":false,"given":"Karoline","family":"Faust","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"key":"5651_CR1","doi-asserted-by":"publisher","first-page":"3111","DOI":"10.1038\/s41396-021-01027-4","volume":"15","author":"K Faust","year":"2021","unstructured":"Faust K. Open challenges for microbial network construction and analysis. ISME J. 2021;15:3111\u20138.","journal-title":"ISME J"},{"key":"5651_CR2","doi-asserted-by":"publisher","first-page":"e8157","DOI":"10.15252\/msb.20178157","volume":"14","author":"OS Venturelli","year":"2018","unstructured":"Venturelli OS, Carr AV, Fisher G, Hsu RH, Lau R, Bowen BP, et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol Syst Biol. 2018;14:e8157.","journal-title":"Mol Syst Biol"},{"key":"5651_CR3","doi-asserted-by":"publisher","first-page":"2332","DOI":"10.1093\/bioinformatics\/bty941","volume":"35","author":"F Baldini","year":"2019","unstructured":"Baldini F, Heinken A, Heirendt L, Magnusdottir S, Fleming RMT, Thiele I. The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities. Bioinformatics. 2019;35:2332\u20134.","journal-title":"Bioinformatics"},{"key":"5651_CR4","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1038\/ncomms1597","volume":"2","author":"S Freilich","year":"2011","unstructured":"Freilich S, Zarecki R, Eilam O, Segal ES, Henry CS, Kupiec M, et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat Commun. 2011;2:589.","journal-title":"Nat Commun"},{"key":"5651_CR5","doi-asserted-by":"publisher","DOI":"10.1101\/2020.01.28.922583","author":"D Machado","year":"2020","unstructured":"Machado D, Maistrenko OM, Andrejev S, Kim Y, Bork P, Patil KR, et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat Ecol Evol. 2020. https:\/\/doi.org\/10.1101\/2020.01.28.922583.","journal-title":"Nat Ecol Evol"},{"key":"5651_CR6","doi-asserted-by":"publisher","first-page":"7542","DOI":"10.1093\/nar\/gky537","volume":"46","author":"D Machado","year":"2018","unstructured":"Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 2018;46:7542\u201353.","journal-title":"Nucleic Acids Res"},{"key":"5651_CR7","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkaa1143","author":"SMD Seaver","year":"2021","unstructured":"Seaver SMD, Liu F, Zhang Q, Jeffryes J, Faria JP, Edirisinghe JN, et al. The ModelSEED biochemistry database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res. 2021. https:\/\/doi.org\/10.1093\/nar\/gkaa1143.","journal-title":"Nucleic Acids Res"},{"key":"5651_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-021-02295-1","volume":"22","author":"J Zimmermann","year":"2021","unstructured":"Zimmermann J, Kaleta C, Waschina S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 2021;22:1\u201335.","journal-title":"Genome Biol"},{"key":"5651_CR9","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1038\/nprot.2009.203","volume":"5","author":"I Thiele","year":"2010","unstructured":"Thiele I, Palsson B\u00d8. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5:93\u2013121.","journal-title":"Nat Protoc"},{"key":"5651_CR10","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1038\/s41587-020-0446-y","volume":"38","author":"C Lieven","year":"2020","unstructured":"Lieven C, Beber ME, Olivier BG, Bergmann FT, Ataman M, Babaei P, et al. MEMOTE for standardized genome-scale metabolic model testing. Nat Biotechnol. 2020;38:272\u20136.","journal-title":"Nat Biotechnol"},{"key":"5651_CR11","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/nbt.1614","volume":"28","author":"JD Orth","year":"2010","unstructured":"Orth JD, Thiele I, Palsson B\u00d8. What is flux balance analysis? Nat Biotechnol. 2010;28:245\u20138.","journal-title":"Nat Biotechnol"},{"key":"5651_CR12","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.jtbi.2009.01.027","volume":"258","author":"K Smallbone","year":"2009","unstructured":"Smallbone K, Simeonidis E. Flux balance analysis: a geometric perspective. J Theor Biol. 2009;258:311\u20135.","journal-title":"J Theor Biol"},{"key":"5651_CR13","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.mib.2010.03.003","volume":"13","author":"AM Feist","year":"2010","unstructured":"Feist AM, Palsson BO. The biomass objective function. Curr Opin Microbiol. 2010;13:344\u20139.","journal-title":"Curr Opin Microbiol"},{"key":"5651_CR14","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1038\/msb.2010.47","volume":"6","author":"NE Lewis","year":"2010","unstructured":"Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol. 2010;6:390.","journal-title":"Mol Syst Biol"},{"key":"5651_CR15","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1093\/bib\/bbp011","volume":"10","author":"K Raman","year":"2009","unstructured":"Raman K, Chandra N. Flux balance analysis of biological systems: applications and challenges. Brief Bioinform. 2009;10:435\u201349.","journal-title":"Brief Bioinform"},{"issue":"5","key":"5651_CR16","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1049\/iet-syb.2013.0021","volume":"8","author":"MA Henson","year":"2016","unstructured":"Henson MA, Hanly TJ. Dynamic flux balance analysis for synthetic microbial communities. IET Systems Biol. 2014;8(5):214\u201329.","journal-title":"IET Systems Biol"},{"key":"5651_CR17","doi-asserted-by":"publisher","first-page":"673","DOI":"10.3389\/fmicb.2016.00673","volume":"7","author":"O Perez-Garcia","year":"2016","unstructured":"Perez-Garcia O, Lear G, Singhal N. Metabolic network modeling of microbial interactions in natural and engineered environmental systems. Front Microbiol. 2016;7:673.","journal-title":"Front Microbiol"},{"key":"5651_CR18","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.cels.2022.11.002","volume":"14","author":"DR Garza","year":"2023","unstructured":"Garza DR, Gonze D, Zafeiropoulos H, Liu B, Faust K. Metabolic models of human gut microbiota: advances and challenges. Cell Syst. 2023;14:109\u201321.","journal-title":"Cell Syst"},{"key":"5651_CR19","doi-asserted-by":"publisher","first-page":"e00606","DOI":"10.1128\/mSystems.00606-19","volume":"5","author":"C Diener","year":"2020","unstructured":"Diener C, Gibbons SM, Resendis-Antonio O. MICOM: metagenome-scale modeling to infer metabolic interactions in the gut microbiota. mSystems. 2020;5:e00606\u201319.","journal-title":"mSystems"},{"key":"5651_CR20","doi-asserted-by":"publisher","first-page":"e1005539","DOI":"10.1371\/journal.pcbi.1005539","volume":"13","author":"SHJ Chan","year":"2017","unstructured":"Chan SHJ, Simons MN, Maranas CD. SteadyCom: predicting microbial abundances while ensuring community stability. PLOS Comput Biol. 2017;13:e1005539.","journal-title":"PLOS Comput Biol"},{"key":"5651_CR21","doi-asserted-by":"publisher","first-page":"e1002363","DOI":"10.1371\/journal.pcbi.1002363","volume":"8","author":"AR Zomorrodi","year":"2012","unstructured":"Zomorrodi AR, Maranas CD. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput Biol. 2012;8:e1002363.","journal-title":"PLoS Comput Biol"},{"key":"5651_CR22","doi-asserted-by":"publisher","first-page":"e1005544","DOI":"10.1371\/journal.pcbi.1005544","volume":"13","author":"E Bauer","year":"2017","unstructured":"Bauer E, Zimmermann J, Baldini F, Thiele I, Kaleta C. BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLOS Comput Biol. 2017;13:e1005544.","journal-title":"PLOS Comput Biol"},{"key":"5651_CR23","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1016\/j.celrep.2014.03.070","volume":"7","author":"WR Harcombe","year":"2014","unstructured":"Harcombe WR, Riehl WJ, Dukovski I, Granger BR, Betts A, Lang AH, et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep. 2014;7:1104\u201315.","journal-title":"Cell Rep"},{"key":"5651_CR24","doi-asserted-by":"publisher","first-page":"574","DOI":"10.3389\/fbioe.2020.00574","volume":"8","author":"D Popp","year":"2020","unstructured":"Popp D, Centler F. \u03bcBialSim: constraint-based dynamic simulation of complex microbiomes. Front Bioeng Biotechnol. 2020;8:574.","journal-title":"Front Bioeng Biotechnol"},{"key":"5651_CR25","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1038\/ismej.2010.117","volume":"5","author":"K Zhuang","year":"2011","unstructured":"Zhuang K, Izallalen M, Mouser P, Richter H, Risso C, Mahadevan R, et al. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J. 2011;5:305\u201316.","journal-title":"ISME J"},{"key":"5651_CR26","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1186\/s12859-015-0588-y","volume":"16","author":"R Levy","year":"2015","unstructured":"Levy R, Carr R, Kreimer A, Freilich S, Borenstein E. NetCooperate: a network-based tool for inferring host-microbe and microbe-microbe cooperation. BMC Bioinform. 2015;16:164.","journal-title":"BMC Bioinform"},{"key":"5651_CR27","doi-asserted-by":"crossref","unstructured":"The struggle for existence. In: The science of the struggle for existence. Cambridge University Press; 2003. p. 1\u201326.","DOI":"10.1017\/CBO9780511720154.002"},{"key":"5651_CR28","doi-asserted-by":"publisher","first-page":"6449","DOI":"10.1073\/pnas.1421834112","volume":"112","author":"A Zelezniak","year":"2015","unstructured":"Zelezniak A, Andrejev S, Ponomarova O, Mende DR, Bork P, Patil KR. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl Acad Sci. 2015;112:6449\u201354.","journal-title":"Proc Natl Acad Sci"},{"key":"5651_CR29","doi-asserted-by":"publisher","first-page":"e1003695","DOI":"10.1371\/journal.pcbi.1003695","volume":"10","author":"H-C Chiu","year":"2014","unstructured":"Chiu H-C, Levy R, Borenstein E. Emergent biosynthetic capacity in simple microbial communities. PLoS Comput Biol. 2014;10:e1003695.","journal-title":"PLoS Comput Biol"},{"key":"5651_CR30","doi-asserted-by":"publisher","first-page":"569700","DOI":"10.3389\/fmicb.2020.569700","volume":"11","author":"L Cheng","year":"2020","unstructured":"Cheng L, Kiewiet MBG, Logtenberg MJ, Groeneveld A, Nauta A, Schols HA, et al. Effects of different human milk oligosaccharides on growth of bifidobacteria in monoculture and co-culture with Faecalibacterium prausnitzii. Front Microbiol. 2020;11:569700.","journal-title":"Front Microbiol"},{"key":"5651_CR31","first-page":"569700","volume":"7","author":"K D\u2019hoe","year":"2018","unstructured":"D\u2019hoe K, Vet S, Faust K, Moens F, Falony G, Gonze D, et al. Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a three-species synthetic gut community. eLife. 2018;7:569700.","journal-title":"eLife"},{"key":"5651_CR32","doi-asserted-by":"publisher","first-page":"e0195161","DOI":"10.1371\/journal.pone.0195161","volume":"13","author":"P Das","year":"2018","unstructured":"Das P, Ji B, Kovatcheva-Datchary P, B\u00e4ckhed F, Nielsen J. In vitro co-cultures of human gut bacterial species as predicted from co-occurrence network analysis. PLoS ONE. 2018;13:e0195161.","journal-title":"PLoS ONE"},{"key":"5651_CR33","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1038\/s41396-021-01153-z","volume":"16","author":"AS Weiss","year":"2021","unstructured":"Weiss AS, Burrichter AG, Raj ACD, von Strempel A, Meng C, Kleigrewe K, et al. In vitro interaction network of a synthetic gut bacterial community. ISME J. 2021;16:1095\u2013109.","journal-title":"ISME J"},{"key":"5651_CR34","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.3390\/microorganisms8111703","volume":"8","author":"Y Wang","year":"2020","unstructured":"Wang Y, LaPointe G. Arabinogalactan utilization by Bifidobacterium longum subsp. longum NCC 2705 and Bacteroides caccae ATCC 43185 in monoculture and coculture. Microorganisms. 2020;8:1703.","journal-title":"Microorganisms"},{"key":"5651_CR35","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1038\/nbt.3703","volume":"35","author":"S Magn\u00fasd\u00f3ttir","year":"2017","unstructured":"Magn\u00fasd\u00f3ttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E, Noronha A, et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol. 2017;35:81\u20139.","journal-title":"Nat Biotechnol"},{"key":"5651_CR36","doi-asserted-by":"publisher","first-page":"3289","DOI":"10.1128\/JB.01780-14","volume":"196","author":"A Heinken","year":"2014","unstructured":"Heinken A, Khan MT, Paglia G, Rodionov DA, Harmsen HJM, Thiele I. Functional metabolic map of Faecalibacterium prausnitzii, a beneficial human gut microbe. J Bacteriol. 2014;196:3289\u2013302.","journal-title":"J Bacteriol"},{"key":"5651_CR37","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1080\/19490976.2015.1023494","volume":"6","author":"A Heinken","year":"2015","unstructured":"Heinken A, Thiele I. Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework. Gut Microbes. 2015;6:120\u201330.","journal-title":"Gut Microbes"},{"key":"5651_CR38","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.1128\/AEM.03279-14","volume":"81","author":"N Veith","year":"2015","unstructured":"Veith N, Solheim M, van Grinsven KWA, Olivier BG, Levering J, Grosseholz R, et al. Using a genome-scale metabolic model of Enterococcus faecalis V583 to assess amino acid uptake and its impact on central metabolism. Appl Environ Microbiol. 2015;81:1622\u201333.","journal-title":"Appl Environ Microbiol"},{"key":"5651_CR39","doi-asserted-by":"publisher","first-page":"e01014","DOI":"10.1128\/AEM.01014-17","volume":"83","author":"N Ottman","year":"2017","unstructured":"Ottman N, Davids M, Suarez-Diez M, Boeren S, Schaap PJ, dos Santos VAPM, et al. Genome-scale model and omics analysis of metabolic capacities of Akkermansia muciniphila reveal a preferential mucin-degrading lifestyle. Appl Env Microbiol. 2017;83:e01014\u20137.","journal-title":"Appl Env Microbiol"},{"key":"5651_CR40","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.biosystems.2010.09.011","volume":"103","author":"S-A Marashi","year":"2011","unstructured":"Marashi S-A, Bockmayr A. Flux coupling analysis of metabolic networks is sensitive to missing reactions. Biosystems. 2011;103:57\u201366.","journal-title":"Biosystems"},{"key":"5651_CR41","doi-asserted-by":"publisher","first-page":"15112","DOI":"10.1073\/pnas.232349399","volume":"99","author":"D Segr\u00e8","year":"2002","unstructured":"Segr\u00e8 D, Vitkup D, Church GM. Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci USA. 2002;99:15112\u20137.","journal-title":"Proc Natl Acad Sci USA"},{"key":"5651_CR42","doi-asserted-by":"publisher","first-page":"5030","DOI":"10.1038\/s41596-021-00593-3","volume":"16","author":"I Dukovski","year":"2021","unstructured":"Dukovski I, Baji\u0107 D, Chac\u00f3n JM, Quintin M, Vila JCC, Sulheim S, et al. A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nat Protoc. 2021;16:5030\u201382.","journal-title":"Nat Protoc"},{"key":"5651_CR43","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1016\/S0006-3495(02)73903-9","volume":"83","author":"R Mahadevan","year":"2002","unstructured":"Mahadevan R, Edwards JS, Doyle FJ. Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J. 2002;83:1331\u201340.","journal-title":"Biophys J"},{"key":"5651_CR44","doi-asserted-by":"publisher","first-page":"106040","DOI":"10.1016\/j.isci.2023.106040","volume":"26","author":"F Clasen","year":"2023","unstructured":"Clasen F, Nunes PM, Bidkhori G, Bah N, Boeing S, Shoaie S, et al. Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer. iScience. 2023;26:106040.","journal-title":"iScience"},{"key":"5651_CR45","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1038\/s41596-018-0098-2","volume":"14","author":"L Heirendt","year":"2019","unstructured":"Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc. 2019;14:639\u2013702.","journal-title":"Nat Protoc"},{"key":"5651_CR46","doi-asserted-by":"publisher","first-page":"e46923","DOI":"10.7554\/eLife.46923","volume":"8","author":"MR McLaren","year":"2019","unstructured":"McLaren MR, Willis AD, Callahan BJ. Consistent and correctable bias in metagenomic sequencing experiments. eLife. 2019;8:e46923.","journal-title":"eLife"},{"key":"5651_CR47","first-page":"1313500","volume":"11","author":"L Marrec","year":"2023","unstructured":"Marrec L, Ghenu A-H, Bank C. Challenges and pitfalls of inferring microbial growth rates from lab cultures. Front Ecol Evol. 2023;11:1313500.","journal-title":"Front Ecol Evol"},{"key":"5651_CR48","doi-asserted-by":"publisher","first-page":"eadf5121","DOI":"10.1126\/science.adf5121","volume":"381","author":"M Sch\u00e4fer","year":"2023","unstructured":"Sch\u00e4fer M, Pacheco AR, K\u00fcnzler R, Bortfeld-Miller M, Field CM, Vayena E, et al. Metabolic interaction models recapitulate leaf microbiota ecology. Science. 2023;381:eadf5121.","journal-title":"Science"},{"key":"5651_CR49","doi-asserted-by":"publisher","first-page":"e1006971","DOI":"10.1371\/journal.pcbi.1006971","volume":"15","author":"J-C Lachance","year":"2019","unstructured":"Lachance J-C, Lloyd CJ, Monk JM, Yang L, Sastry AV, Seif Y, et al. BOFdat: generating biomass objective functions for genome-scale metabolic models from experimental data. PLoS Comput Biol. 2019;15:e1006971.","journal-title":"PLoS Comput Biol"},{"key":"5651_CR50","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s40168-020-00955-1","volume":"9","author":"J Jansma","year":"2021","unstructured":"Jansma J, El Aidy S. Understanding the host-microbe interactions using metabolic modeling. Microbiome. 2021;9:16.","journal-title":"Microbiome"},{"key":"5651_CR51","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1038\/nbt.4212","volume":"36","author":"S Magn\u00fasd\u00f3ttir","year":"2018","unstructured":"Magn\u00fasd\u00f3ttir S, Heinken A, Fleming RMT, Thiele I. Reply to \u201cChallenges in modeling the human gut microbiome.\u201d Nat Biotechnol. 2018;36:686\u201391.","journal-title":"Nat Biotechnol"},{"key":"5651_CR52","doi-asserted-by":"publisher","first-page":"e1011363","DOI":"10.1371\/journal.pcbi.1011363","volume":"19","author":"WT Scott Jr","year":"2023","unstructured":"Scott WT Jr, Benito-Vaquerizo S, Zimmermann J, Baji\u0107 D, Heinken A, Suarez-Diez M, et al. A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia. PLoS Comput Biol. 2023;19:e1011363.","journal-title":"PLoS Comput Biol"},{"key":"5651_CR53","doi-asserted-by":"publisher","first-page":"e0171744","DOI":"10.1371\/journal.pone.0171744","volume":"12","author":"M Budinich","year":"2017","unstructured":"Budinich M, Bourdon J, Larhlimi A, Eveillard D. A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems. PLoS ONE. 2017;12:e0171744.","journal-title":"PLoS ONE"},{"key":"5651_CR54","doi-asserted-by":"publisher","first-page":"4049","DOI":"10.1128\/AEM.00101-15","volume":"81","author":"A Heinken","year":"2015","unstructured":"Heinken A, Thiele I. Anoxic conditions promote species-specific mutualism between gut microbes in silico. Appl Env Microbiol. 2015;81:4049\u201361.","journal-title":"Appl Env Microbiol"},{"key":"5651_CR55","doi-asserted-by":"publisher","first-page":"2195","DOI":"10.1093\/bioinformatics\/bts323","volume":"28","author":"A Kreimer","year":"2012","unstructured":"Kreimer A, Doron-Faigenboim A, Borenstein E, Freilich S. NetCmpt: a network-based tool for calculating the metabolic competition between bacterial species. Bioinformatics. 2012;28:2195\u20137.","journal-title":"Bioinformatics"},{"key":"5651_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-016-1088-4","volume":"17","author":"Y Cao","year":"2016","unstructured":"Cao Y, Wang Y, Zheng X, Li F, Bo X. RevEcoR: an R package for the reverse ecology analysis of microbiomes. BMC Bioinform. 2016;17:1\u20136.","journal-title":"BMC Bioinform"},{"key":"5651_CR57","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.ymben.2015.10.003","volume":"32","author":"A Goelzer","year":"2015","unstructured":"Goelzer A, Muntel J, Chubukov V, Jules M, Prestel E, N\u00f6lker R, et al. Quantitative prediction of genome-wide resource allocation in bacteria. Metab Eng. 2015;32:232\u201343.","journal-title":"Metab Eng"},{"key":"5651_CR58","doi-asserted-by":"publisher","first-page":"935","DOI":"10.15252\/msb.20167411","volume":"13","author":"BJ S\u00e1nchez","year":"2017","unstructured":"S\u00e1nchez BJ, Zhang C, Nilsson A, Lahtvee P-J, Kerkhoven EJ, Nielsen J. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol Syst Biol. 2017;13:935.","journal-title":"Mol Syst Biol"},{"key":"5651_CR59","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1038\/s42255-018-0006-7","volume":"1","author":"B Niebel","year":"2019","unstructured":"Niebel B, Leupold S, Heinemann M. An upper limit on Gibbs energy dissipation governs cellular metabolism. Nat Metab. 2019;1:125\u201332.","journal-title":"Nat Metab"},{"key":"5651_CR60","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1038\/s41587-022-01628-0","volume":"41","author":"A Heinken","year":"2023","unstructured":"Heinken A, Hertel J, Acharya G, Ravcheev DA, Nyga M, Okpala OE, et al. Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine. Nat Biotechnol. 2023;41:1320\u201331.","journal-title":"Nat Biotechnol"},{"key":"5651_CR61","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1099\/ijs.0.64483-0","volume":"57","author":"J Goris","year":"2007","unstructured":"Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, Tiedje JM. DNA\u2013DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol. 2007;57:81\u201391.","journal-title":"Int J Syst Evol Microbiol"},{"key":"5651_CR62","doi-asserted-by":"publisher","first-page":"5114","DOI":"10.1038\/s41467-018-07641-9","volume":"9","author":"C Jain","year":"2018","unstructured":"Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.","journal-title":"Nat Commun"},{"key":"5651_CR63","doi-asserted-by":"publisher","first-page":"e0236890","DOI":"10.1371\/journal.pone.0236890","volume":"15","author":"G Marinos","year":"2020","unstructured":"Marinos G, Kaleta C, Waschina S. Defining the nutritional input for genome-scale metabolic models: a roadmap. PLoS ONE. 2020;15:e0236890.","journal-title":"PLoS ONE"},{"key":"5651_CR64","unstructured":"Computation and analysis of microbe-microbe metabolic interactions. http:\/\/gibbs.unal.edu.co\/cobradoc\/cobratoolbox\/tutorials\/analysis\/microbeMicrobeInteractions\/iframe_tutorial_microbeMicrobeInteractions.html. Accessed 5 Sep 2023."},{"key":"5651_CR65","unstructured":"Micom documentation. https:\/\/micom-dev.github.io\/micom\/. Accessed 5 Sep 2023."},{"key":"5651_CR66","unstructured":"Growth in a test tube\u2014COMETS documentation. https:\/\/segrelab.github.io\/comets-manual\/test_tube\/. Accessed 5 Sep 2023."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-05651-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-024-05651-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-05651-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T17:02:40Z","timestamp":1706029360000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-024-05651-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,23]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["5651"],"URL":"https:\/\/doi.org\/10.1186\/s12859-024-05651-7","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,23]]},"assertion":[{"value":"23 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that there are no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"36"}}