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Cited by in F6Publishing
For: Shoaie S, Karlsson F, Mardinoglu A, Nookaew I, Bordel S, Nielsen J. Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci Rep. 2013;3:2532. [PMID: 23982459 DOI: 10.1038/srep02532] [Cited by in Crossref: 149] [Cited by in F6Publishing: 127] [Article Influence: 21.3] [Reference Citation Analysis]
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11 Cӑtoi AF, Corina A, Katsiki N, Vodnar DC, Andreicuț AD, Stoian AP, Rizzo M, Pérez-martínez P. Gut microbiota and aging-A focus on centenarians. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease 2020;1866:165765. [DOI: 10.1016/j.bbadis.2020.165765] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 10.0] [Reference Citation Analysis]
12 Sen P, Orešič M. Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview. Metabolites 2019;9:E22. [PMID: 30695998 DOI: 10.3390/metabo9020022] [Cited by in Crossref: 39] [Cited by in F6Publishing: 21] [Article Influence: 19.5] [Reference Citation Analysis]
13 Bernal V, Castaño-cerezo S, Cánovas M. Acetate metabolism regulation in Escherichia coli: carbon overflow, pathogenicity, and beyond. Appl Microbiol Biotechnol 2016;100:8985-9001. [DOI: 10.1007/s00253-016-7832-x] [Cited by in Crossref: 39] [Cited by in F6Publishing: 33] [Article Influence: 7.8] [Reference Citation Analysis]
14 Bado M, Kwende S, Shishodia S, Rosenzweig JA. Impact of dust exposure on mixed bacterial cultures and during eukaryotic cell co-culture infections. Appl Microbiol Biotechnol 2017;101:7027-39. [PMID: 28776099 DOI: 10.1007/s00253-017-8449-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
15 Bui TPN, Schols HA, Jonathan M, Stams AJM, de Vos WM, Plugge CM. Mutual Metabolic Interactions in Co-cultures of the Intestinal Anaerostipes rhamnosivorans With an Acetogen, Methanogen, or Pectin-Degrader Affecting Butyrate Production. Front Microbiol 2019;10:2449. [PMID: 31736896 DOI: 10.3389/fmicb.2019.02449] [Cited by in Crossref: 13] [Cited by in F6Publishing: 7] [Article Influence: 6.5] [Reference Citation Analysis]
16 Spacova I, Dodiya HB, Happel AU, Strain C, Vandenheuvel D, Wang X, Reid G. Future of Probiotics and Prebiotics and the Implications for Early Career Researchers. Front Microbiol 2020;11:1400. [PMID: 32714306 DOI: 10.3389/fmicb.2020.01400] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 7.0] [Reference Citation Analysis]
17 Biggs MB, Medlock GL, Kolling GL, Papin JA. Metabolic network modeling of microbial communities. Wiley Interdiscip Rev Syst Biol Med 2015;7:317-34. [PMID: 26109480 DOI: 10.1002/wsbm.1308] [Cited by in Crossref: 64] [Cited by in F6Publishing: 51] [Article Influence: 10.7] [Reference Citation Analysis]
18 Pornputtapong N, Nookaew I, Nielsen J. Human metabolic atlas: an online resource for human metabolism. Database (Oxford) 2015;2015:bav068. [PMID: 26209309 DOI: 10.1093/database/bav068] [Cited by in Crossref: 43] [Cited by in F6Publishing: 35] [Article Influence: 7.2] [Reference Citation Analysis]
19 Motelica-Wagenaar AM, Nauta A, van den Heuvel EG, Kleerebezem R. Flux analysis of the human proximal colon using anaerobic digestion model 1. Anaerobe 2014;28:137-48. [PMID: 24880006 DOI: 10.1016/j.anaerobe.2014.05.008] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 1.1] [Reference Citation Analysis]
20 Chellapandi P, Bharathi M, Sangavai C, Prathiviraj R. Methanobacterium formicicum as a target rumen methanogen for the development of new methane mitigation interventions: A review. Vet Anim Sci 2018;6:86-94. [PMID: 32734058 DOI: 10.1016/j.vas.2018.09.001] [Cited by in Crossref: 12] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
21 Kumar M, Babaei P, Ji B, Nielsen J. Human gut microbiota and healthy aging: Recent developments and future prospective. Nutr Healthy Aging. 2016;4:3-16. [PMID: 28035338 DOI: 10.3233/nha-150002] [Cited by in Crossref: 70] [Cited by in F6Publishing: 30] [Article Influence: 14.0] [Reference Citation Analysis]
22 Devika NT, Raman K. Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models. Sci Rep 2019;9:18222. [PMID: 31796826 DOI: 10.1038/s41598-019-54696-9] [Cited by in Crossref: 26] [Cited by in F6Publishing: 7] [Article Influence: 13.0] [Reference Citation Analysis]
23 Kreft JU, Plugge CM, Prats C, Leveau JHJ, Zhang W, Hellweger FL. From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality. Front Microbiol 2017;8:2299. [PMID: 29230200 DOI: 10.3389/fmicb.2017.02299] [Cited by in Crossref: 21] [Cited by in F6Publishing: 9] [Article Influence: 5.3] [Reference Citation Analysis]
24 Mendes-Soares H, Mundy M, Soares LM, Chia N. MMinte: an application for predicting metabolic interactions among the microbial species in a community. BMC Bioinformatics 2016;17:343. [PMID: 27590448 DOI: 10.1186/s12859-016-1230-3] [Cited by in Crossref: 43] [Cited by in F6Publishing: 32] [Article Influence: 8.6] [Reference Citation Analysis]
25 Zhang W, Zhang H, Yu C, Fang J, Ying H. Ethanol extract of Atractylodis macrocephalae Rhizoma ameliorates insulin resistance and gut microbiota in type 2 diabetic db/db mice. Journal of Functional Foods 2017;39:139-51. [DOI: 10.1016/j.jff.2017.10.020] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
26 Heinken A, Thiele I. Systems biology of host-microbe metabolomics. Wiley Interdiscip Rev Syst Biol Med 2015;7:195-219. [PMID: 25929487 DOI: 10.1002/wsbm.1301] [Cited by in Crossref: 51] [Cited by in F6Publishing: 40] [Article Influence: 8.5] [Reference Citation Analysis]
27 Chan SHJ, Simons MN, Maranas CD. SteadyCom: Predicting microbial abundances while ensuring community stability. PLoS Comput Biol 2017;13:e1005539. [PMID: 28505184 DOI: 10.1371/journal.pcbi.1005539] [Cited by in Crossref: 81] [Cited by in F6Publishing: 49] [Article Influence: 20.3] [Reference Citation Analysis]
28 Van Wey AS, Cookson AL, Roy NC, McNabb WC, Soboleva TK, Shorten PR. Monoculture parameters successfully predict coculture growth kinetics of Bacteroides thetaiotaomicron and two Bifidobacterium strains. Int J Food Microbiol 2014;191:172-81. [PMID: 25282609 DOI: 10.1016/j.ijfoodmicro.2014.09.006] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 1.6] [Reference Citation Analysis]
29 Witherden EA, Moyes DL, Bruce KD, Ehrlich SD, Shoaie S. Using systems biology approaches to elucidate cause and effect in host–microbiome interactions. Current Opinion in Systems Biology 2017;3:141-6. [DOI: 10.1016/j.coisb.2017.05.003] [Cited by in Crossref: 9] [Cited by in F6Publishing: 3] [Article Influence: 2.3] [Reference Citation Analysis]
30 Mardinoglu A, Heiker JT, Gärtner D, Björnson E, Schön MR, Flehmig G, Klöting N, Krohn K, Fasshauer M, Stumvoll M, Nielsen J, Blüher M. Extensive weight loss reveals distinct gene expression changes in human subcutaneous and visceral adipose tissue. Sci Rep 2015;5:14841. [PMID: 26434764 DOI: 10.1038/srep14841] [Cited by in Crossref: 44] [Cited by in F6Publishing: 42] [Article Influence: 7.3] [Reference Citation Analysis]
31 Del Chierico F, Gnani D, Vernocchi P, Petrucca A, Alisi A, Dallapiccola B, Nobili V, Lorenza P. Meta-omic platforms to assist in the understanding of NAFLD gut microbiota alterations: tools and applications. Int J Mol Sci. 2014;15:684-711. [PMID: 24402126 DOI: 10.3390/ijms15010684] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 2.7] [Reference Citation Analysis]
32 Lanigan N, Kelly E, Arzamasov AA, Stanton C, Rodionov DA, van Sinderen D. Transcriptional control of central carbon metabolic flux in Bifidobacteria by two functionally similar, yet distinct LacI-type regulators. Sci Rep 2019;9:17851. [PMID: 31780796 DOI: 10.1038/s41598-019-54229-4] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
33 Krishnan S, Alden N, Lee K. Pathways and functions of gut microbiota metabolism impacting host physiology. Curr Opin Biotechnol 2015;36:137-45. [PMID: 26340103 DOI: 10.1016/j.copbio.2015.08.015] [Cited by in Crossref: 90] [Cited by in F6Publishing: 73] [Article Influence: 15.0] [Reference Citation Analysis]
34 Chan, Friedman, Wu, Maranas. Predicting the Longitudinally and Radially Varying Gut Microbiota Composition Using Multi-Scale Microbial Metabolic Modeling. Processes 2019;7:394. [DOI: 10.3390/pr7070394] [Cited by in Crossref: 10] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
35 Lamas B, Martins Breyner N, Houdeau E. Impacts of foodborne inorganic nanoparticles on the gut microbiota-immune axis: potential consequences for host health. Part Fibre Toxicol 2020;17:19. [PMID: 32487227 DOI: 10.1186/s12989-020-00349-z] [Cited by in Crossref: 31] [Cited by in F6Publishing: 19] [Article Influence: 31.0] [Reference Citation Analysis]
36 Wang Y, Ames NP, Tun HM, Tosh SM, Jones PJ, Khafipour E. High Molecular Weight Barley β-Glucan Alters Gut Microbiota Toward Reduced Cardiovascular Disease Risk. Front Microbiol 2016;7:129. [PMID: 26904005 DOI: 10.3389/fmicb.2016.00129] [Cited by in Crossref: 44] [Cited by in F6Publishing: 45] [Article Influence: 8.8] [Reference Citation Analysis]
37 Bosi E, Bacci G, Mengoni A, Fondi M. Perspectives and Challenges in Microbial Communities Metabolic Modeling. Front Genet 2017;8:88. [PMID: 28680442 DOI: 10.3389/fgene.2017.00088] [Cited by in Crossref: 23] [Cited by in F6Publishing: 11] [Article Influence: 5.8] [Reference Citation Analysis]
38 Gou M, Hu HW, Zhang YJ, Wang JT, Hayden H, Tang YQ, He JZ. Aerobic composting reduces antibiotic resistance genes in cattle manure and the resistome dissemination in agricultural soils. Sci Total Environ 2018;612:1300-10. [PMID: 28898936 DOI: 10.1016/j.scitotenv.2017.09.028] [Cited by in Crossref: 103] [Cited by in F6Publishing: 79] [Article Influence: 25.8] [Reference Citation Analysis]
39 Hahn AS, Konwar KM, Louca S, Hanson NW, Hallam SJ. The information science of microbial ecology. Curr Opin Microbiol 2016;31:209-16. [PMID: 27183115 DOI: 10.1016/j.mib.2016.04.014] [Cited by in Crossref: 19] [Cited by in F6Publishing: 7] [Article Influence: 3.8] [Reference Citation Analysis]
40 Gomes de Oliveira Dal'Molin C, Quek LE, Saa PA, Nielsen LK. A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems. Front Plant Sci 2015;6:4. [PMID: 25657653 DOI: 10.3389/fpls.2015.00004] [Cited by in Crossref: 28] [Cited by in F6Publishing: 34] [Article Influence: 4.7] [Reference Citation Analysis]
41 Fouladiha H, Marashi S. Biomedical applications of cell- and tissue-specific metabolic network models. Journal of Biomedical Informatics 2017;68:35-49. [DOI: 10.1016/j.jbi.2017.02.014] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 3.8] [Reference Citation Analysis]
42 Liang D, Leung RK, Guan W, Au WW. Involvement of gut microbiome in human health and disease: brief overview, knowledge gaps and research opportunities. Gut Pathog. 2018;10:3. [PMID: 29416567 DOI: 10.1186/s13099-018-0230-4] [Cited by in Crossref: 79] [Cited by in F6Publishing: 53] [Article Influence: 26.3] [Reference Citation Analysis]
43 Shoaie S, Nielsen J. Elucidating the interactions between the human gut microbiota and its host through metabolic modeling. Front Genet. 2014;5:86. [PMID: 24795748 DOI: 10.3389/fgene.2014.00086] [Cited by in Crossref: 56] [Cited by in F6Publishing: 52] [Article Influence: 8.0] [Reference Citation Analysis]
44 Rau MH, Zeidan AA. Constraint-based modeling in microbial food biotechnology. Biochem Soc Trans 2018;46:249-60. [PMID: 29588387 DOI: 10.1042/BST20170268] [Cited by in Crossref: 14] [Cited by in F6Publishing: 5] [Article Influence: 4.7] [Reference Citation Analysis]
45 Gottstein W, Olivier BG, Bruggeman FJ, Teusink B. Constraint-based stoichiometric modelling from single organisms to microbial communities. J R Soc Interface 2016;13:20160627. [PMID: 28334697 DOI: 10.1098/rsif.2016.0627] [Cited by in Crossref: 51] [Cited by in F6Publishing: 35] [Article Influence: 12.8] [Reference Citation Analysis]
46 Kim J, Reed JL. Refining metabolic models and accounting for regulatory effects. Curr Opin Biotechnol 2014;29:34-8. [PMID: 24632483 DOI: 10.1016/j.copbio.2014.02.009] [Cited by in Crossref: 20] [Cited by in F6Publishing: 13] [Article Influence: 2.9] [Reference Citation Analysis]
47 Henson MA, Phalak P. Suboptimal community growth mediated through metabolite crossfeeding promotes species diversity in the gut microbiota. PLoS Comput Biol 2018;14:e1006558. [PMID: 30376571 DOI: 10.1371/journal.pcbi.1006558] [Cited by in Crossref: 14] [Cited by in F6Publishing: 7] [Article Influence: 4.7] [Reference Citation Analysis]
48 Di Lodovico L, Mondot S, Doré J, Mack I, Hanachi M, Gorwood P. Anorexia nervosa and gut microbiota: A systematic review and quantitative synthesis of pooled microbiological data. Prog Neuropsychopharmacol Biol Psychiatry 2021;106:110114. [PMID: 32971217 DOI: 10.1016/j.pnpbp.2020.110114] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
49 Araújo FO, Moreira MEC, Lima CF, Toledo RCL, de Sousa AR, Veloso MP, de Freitas PG, Dos Santos MH, de Souza ECG, Mantovani HC, Martino HSD. Bacupari (Garcinia brasiliensis) extract modulates intestinal microbiota and reduces oxidative stress and inflammation in obese rats. Food Res Int 2019;122:199-208. [PMID: 31229073 DOI: 10.1016/j.foodres.2019.04.012] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
50 Li C, Lim KM, Chng KR, Nagarajan N. Predicting microbial interactions through computational approaches. Methods 2016;102:12-9. [PMID: 27025964 DOI: 10.1016/j.ymeth.2016.02.019] [Cited by in Crossref: 31] [Cited by in F6Publishing: 12] [Article Influence: 6.2] [Reference Citation Analysis]
51 Mardinoglu A, Shoaie S, Bergentall M, Ghaffari P, Zhang C, Larsson E, Bäckhed F, Nielsen J. The gut microbiota modulates host amino acid and glutathione metabolism in mice. Mol Syst Biol 2015;11:834. [PMID: 26475342 DOI: 10.15252/msb.20156487] [Cited by in Crossref: 182] [Cited by in F6Publishing: 158] [Article Influence: 30.3] [Reference Citation Analysis]
52 Chong J, Xia J. Computational Approaches for Integrative Analysis of the Metabolome and Microbiome. Metabolites 2017;7:E62. [PMID: 29156542 DOI: 10.3390/metabo7040062] [Cited by in Crossref: 50] [Cited by in F6Publishing: 34] [Article Influence: 12.5] [Reference Citation Analysis]
53 Chowdhury S, Fong SS. Computational Modeling of the Human Microbiome. Microorganisms 2020;8:E197. [PMID: 32023941 DOI: 10.3390/microorganisms8020197] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
54 Bryant WA, Stentz R, Le Gall G, Sternberg MJE, Carding SR, Wilhelm T. In Silico Analysis of the Small Molecule Content of Outer Membrane Vesicles Produced by Bacteroides thetaiotaomicron Indicates an Extensive Metabolic Link between Microbe and Host. Front Microbiol 2017;8:2440. [PMID: 29276507 DOI: 10.3389/fmicb.2017.02440] [Cited by in Crossref: 23] [Cited by in F6Publishing: 18] [Article Influence: 5.8] [Reference Citation Analysis]
55 Clendenen N, Nunns GR, Moore EE, Gonzalez E, Chapman M, Reisz JA, Peltz E, Fragoso M, Nemkov T, Wither MJ, Sauaia A, Silliman CC, Hansen K, Banerjee A, D'Alessandro A, Moore HB. Selective organ ischaemia/reperfusion identifies liver as the key driver of the post-injury plasma metabolome derangements. Blood Transfus 2019;17:347-56. [PMID: 30747701 DOI: 10.2450/2018.0188-18] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
56 Mardinoglu A, Agren R, Kampf C, Asplund A, Uhlen M, Nielsen J. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun. 2014;5:3083. [PMID: 24419221 DOI: 10.1038/ncomms4083] [Cited by in Crossref: 304] [Cited by in F6Publishing: 245] [Article Influence: 50.7] [Reference Citation Analysis]
57 Shetty SA, Smidt H, de Vos WM. Reconstructing functional networks in the human intestinal tract using synthetic microbiomes. Curr Opin Biotechnol 2019;58:146-54. [PMID: 30959425 DOI: 10.1016/j.copbio.2019.03.009] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 7.5] [Reference Citation Analysis]
58 Magnúsdóttir S, Thiele I. Modeling metabolism of the human gut microbiome. Curr Opin Biotechnol 2018;51:90-6. [PMID: 29258014 DOI: 10.1016/j.copbio.2017.12.005] [Cited by in Crossref: 72] [Cited by in F6Publishing: 45] [Article Influence: 18.0] [Reference Citation Analysis]
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60 Proffitt C, Bidkhori G, Moyes D, Shoaie S. Disease, Drugs and Dysbiosis: Understanding Microbial Signatures in Metabolic Disease and Medical Interventions. Microorganisms 2020;8:E1381. [PMID: 32916966 DOI: 10.3390/microorganisms8091381] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
61 Goyal A, Wang T, Dubinkina V, Maslov S. Ecology-guided prediction of cross-feeding interactions in the human gut microbiome. Nat Commun 2021;12:1335. [PMID: 33637740 DOI: 10.1038/s41467-021-21586-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
62 Bruce-Keller AJ, Fernandez-Kim SO, Townsend RL, Kruger C, Carmouche R, Newman S, Salbaum JM, Berthoud HR. Maternal obese-type gut microbiota differentially impact cognition, anxiety and compulsive behavior in male and female offspring in mice. PLoS One 2017;12:e0175577. [PMID: 28441394 DOI: 10.1371/journal.pone.0175577] [Cited by in Crossref: 30] [Cited by in F6Publishing: 27] [Article Influence: 7.5] [Reference Citation Analysis]
63 Islam MM, Fernando SC, Saha R. Metabolic Modeling Elucidates the Transactions in the Rumen Microbiome and the Shifts Upon Virome Interactions. Front Microbiol 2019;10:2412. [PMID: 31866953 DOI: 10.3389/fmicb.2019.02412] [Cited by in Crossref: 10] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
64 Iversen H, Lindbäck T, L'Abée-Lund TM, Roos N, Aspholm M, Stenfors Arnesen L. The gut bacterium Bacteroides thetaiotaomicron influences the virulence potential of the enterohemorrhagic Escherichia coli O103:H25. PLoS One 2015;10:e0118140. [PMID: 25719195 DOI: 10.1371/journal.pone.0118140] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 2.2] [Reference Citation Analysis]
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