Modeling approaches for probing cross-feeding interactions in the human gut microbiome
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Nayfach, 2019, New insights from uncultivated genomes of the global human gut microbiome, Nature, 568, 505, 10.1038/s41586-019-1058-x
Gasaly, 2021, Impact of bacterial metabolites on gut barrier function and host immunity: A focus on bacterial metabolism and its relevance for intestinal inflammation, Front Immunol, 12, 1807, 10.3389/fimmu.2021.658354
Cronin, 2021, Dietary fibre modulates the gut microbiota, Nutrients, 13, 1655, 10.3390/nu13051655
Coyte, 2015, The ecology of the microbiome: Networks, competition, and stability, Science, 350, 663, 10.1126/science.aad2602
Tsukuda, 2021, Key bacterial taxa and metabolic pathways affecting gut short-chain fatty acid profiles in early life, ISME J, 15, 2574, 10.1038/s41396-021-00937-7
Aires, 2021, First 1000 days of life: Consequences of antibiotics on gut microbiota, Front Microbiol, 12, 681427, 10.3389/fmicb.2021.681427
Sommer, 2017, The resilience of the intestinal microbiota influences health and disease, Nat Rev Microbiol, 15, 630, 10.1038/nrmicro.2017.58
Goyal, 2021, Ecology-guided prediction of cross-feeding interactions in the human gut microbiome, Nat Commun, 12, 1335, 10.1038/s41467-021-21586-6
Clark, 2021, Design of synthetic human gut microbiome assembly and butyrate production, Nat Commun, 12, 10.1038/s41467-021-22938-y
Gutiérrez, 2019, Species deletions from microbiome consortia reveal key metabolic interactions between gut microbes, MSystems, 4, 10.1128/mSystems.00185-19
Pacheco, 2019, A multidimensional perspective on microbial interactions, FEMS Microbiol Lett, 366, 10.1093/femsle/fnz125
Coquant, 2020, Impact of N-Acyl-homoserine lactones, quorum sensing molecules, on gut immunity, Front Immunol, 11, 1827, 10.3389/fimmu.2020.01827
Ding, 2021, Crosstalk between sIgA-coated bacteria in infant gut and early-life health, Trends Microbiol, 29, 725, 10.1016/j.tim.2021.01.012
García-Bayona, 2021, Mobile Type VI secretion system loci of the gut Bacteroidales display extensive intra-ecosystem transfer, multi-species spread and geographical clustering, PLOS Genet, 17, e1009541, 10.1371/journal.pgen.1009541
Gong, 2021, Antimicrobial peptides in gut health: A review, Front Nutr, 8, 711, 10.3389/fnut.2021.751010
Huus, 2021, Diversity and dynamism of IgA−microbiota interactions, Nat Rev Immunol, 21, 514, 10.1038/s41577-021-00506-1
Donaldson, 2016, Gut biogeography of the bacterial microbiota, Nat Rev Microbiol, 14, 20, 10.1038/nrmicro3552
Klymiuk, 2021, Characterization of the luminal and mucosa-associated microbiome along the gastrointestinal tract: results from surgically treated preterm infants and a murine model, Nutr, 13, 1030
Kettle, 2015, Modelling the emergent dynamics and major metabolites of the human colonic microbiota, Environ Microbiol, 17, 1615, 10.1111/1462-2920.12599
Louis, 2017, Formation of propionate and butyrate by the human colonic microbiota, Environ Microbiol, 19, 29, 10.1111/1462-2920.13589
D'Souza, 2018, Ecology and evolution of metabolic cross-feeding interactions in bacteria, Nat Prod Rep, 35, 455, 10.1039/C8NP00009C
Belzer, 2017, Microbial metabolic networks at the mucus layer lead to diet-independent butyrate and vitamin B 12 production by intestinal symbionts, mBio, 8, 10.1128/mBio.00770-17
Sung, 2017, Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis, Nat Commun, 8, 10.1038/ncomms15393
Saa, 2017, Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks, Biotechnol Adv, 35, 981, 10.1016/j.biotechadv.2017.09.005
Altamirano, 2020, Inferring composition and function of the human gut microbiome in time and space: A review of genome-scale metabolic modelling tools, Comput Struct Biotechnol J, 18, 3897, 10.1016/j.csbj.2020.11.035
Lewis, 2012, Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods, Nat Rev Microbiol, 10, 291, 10.1038/nrmicro2737
Zomorrodi, 2012, A multi-level optimization framework for the metabolic modeling and analysis of microbial communities, PLoS Comput Biol, 8, e1002363, 10.1371/journal.pcbi.1002363
Heirendt, 2019, Creation and analysis of biochemical constraint-based models using the COBRA Toolbox vol 3.0, Nat Protoc, 14, 639, 10.1038/s41596-018-0098-2
Zomorrodi, 2014, D-OptCom: dynamic multi-level and multi-objective metabolic modeling of microbial communities, ACS Synth Biol, 3, 247, 10.1021/sb4001307
Heinken, 2021, Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis, Npj Syst Biol Appl, 7, 19, 10.1038/s41540-021-00178-6
Baldini, 2020, Parkinson’s disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions, BMC Biol, 18, 10.1186/s12915-020-00775-7
Hertel, 2019, Integrated analyses of microbiome and longitudinal metabolome data reveal microbial-host interactions on sulfur metabolism in Parkinson’s Disease, Cell Rep, 29, 1767, 10.1016/j.celrep.2019.10.035
Renwick, 2021, Culturing human gut microbiomes in the laboratory, Annu Rev Microbiol, 75, 49, 10.1146/annurev-micro-031021-084116
Strandwitz, 2019, GABA-modulating bacteria of the human gut microbiota, Nat Microbiol, 4, 396, 10.1038/s41564-018-0307-3
Bengtsson-Palme, 2020, Microbial model communities: To understand complexity, harness the power of simplicity, Comput Struct Biotechnol J, 18, 3987, 10.1016/j.csbj.2020.11.043
Van den Abbeele, 2013, Butyrate-producing Clostridium cluster XIVa species specifically colonize mucins in an in vitro gut model, ISME J, 7, 949, 10.1038/ismej.2012.158
Medina, 2018, Simulation and modeling of dietary changes in the infant gut microbiome, FEMS Microbiol Ecol, 94
Kim, 2012, Human gut-on-a-chip inhabited by microbial flora that experiences intestinal peristalsis-like motions and flow, Lab Chip, 12, 2165, 10.1039/c2lc40074j
Molly, 1993, Development of a 5-step multi-chamber reactor as a simulation of the human intestinal microbial ecosystem, Appl Microbiol Biotechnol, 39, 254, 10.1007/BF00228615
Marzorati, 2014, The HMITM module: a new tool to study the Host-Microbiota Interaction in the human gastrointestinal tract in vitro, BMC Microbiol, 14, 133, 10.1186/1471-2180-14-133
Shah, 2016, A microfluidics-based in vitro model of the gastrointestinal human–microbe interface, Nat Commun, 7, 10.1038/ncomms11535
Shoaie, 2015, Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome, Cell Metab, 22, 320, 10.1016/j.cmet.2015.07.001
Shoaie, 2013, Understanding the interactions between bacteria in the human gut through metabolic modeling, Sci Rep, 3, 10.1038/srep02532
Everard, 2013, Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity, Proc Natl Acad Sci USA, 110, 9066, 10.1073/pnas.1219451110
Egan, 2014, Cross-feeding by Bifidobacterium breve UCC2003 during co-cultivation with Bifidobacterium bifidum PRL2010 in a mucin-based medium, BMC Microbiol, 14, 10.1186/s12866-014-0282-7
Bunesova, 2018, Mucin cross-feeding of infant Bifidobacteria and Eubacterium hallii, Microb Ecol, 75, 228, 10.1007/s00248-017-1037-4
van Best, 2020, Bile acids drive the newborn’s gut microbiota maturation, Nat Commun, 11, 10.1038/s41467-020-17183-8
Holert, 2014, Evidence of distinct pathways for bacterial degradation of the steroid compound cholate suggests the potential for metabolic interactions by interspecies cross-feeding, Environ Microbiol, 16, 1424, 10.1111/1462-2920.12407
Shetty, 2017, Intestinal microbiome landscaping: Insight in community assemblage and implications for microbial modulation strategies, FEMS Microbiol Rev, 41, 182, 10.1093/femsre/fuw045
Holscher, 2017, Dietary fiber and prebiotics and the gastrointestinal microbiota, Gut Microbes, 8, 172, 10.1080/19490976.2017.1290756
Rivière, 2016, Bifidobacteria and butyrate-producing colon bacteria: Importance and strategies for their stimulation in the human gut, Front Microbiol, 7, 10.3389/fmicb.2016.00979
Laverde Gomez, 2019, Formate cross-feeding and cooperative metabolic interactions revealed by transcriptomics in co-cultures of acetogenic and amylolytic human colonic bacteria, Environ Microbiol, 21, 259, 10.1111/1462-2920.14454
Smith, 2021, Examination of hydrogen cross-feeders using a colonic microbiota model, BMC Bioinf, 22, 3, 10.1186/s12859-020-03923-6
Ravcheev, 2014, Systematic genomic analysis reveals the complementary aerobic and anaerobic respiration capacities of the human gut microbiota, Front Microbiol, 5, 674, 10.3389/fmicb.2014.00674
Cavaliere, 2017, Cooperation in microbial communities and their biotechnological applications, Environ Microbiol, 19, 2949, 10.1111/1462-2920.13767
Rakoff-Nahoum, 2016, The evolution of cooperation within the gut microbiota, Nature, 533, 255, 10.1038/nature17626
Rogowski, 2015, Glycan complexity dictates microbial resource allocation in the large intestine, Nat Commun, 6, 10.1038/ncomms8481
Cuskin, 2015, Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism, Nature, 517, 165, 10.1038/nature13995
Thomson, 2018, Human milk oligosaccharides and infant gut bifidobacteria: Molecular strategies for their utilization, Food Microbiol, 75, 37, 10.1016/j.fm.2017.09.001
Cremer, 2019, Cooperation in microbial populations: theory and experimental model systems, J Mol Biol, 431, 4599, 10.1016/j.jmb.2019.09.023
Damore, 2012, Understanding microbial cooperation, J Theor Biol, 299, 31, 10.1016/j.jtbi.2011.03.008
Magnúsdóttir, 2017, Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota, Nat Biotechnol, 35, 81, 10.1038/nbt.3703
Ghoul, 2016, The ecology and evolution of microbial competition, Trends Microbiol, 24, 833, 10.1016/j.tim.2016.06.011
Duncan, 2004, Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product, Appl Environ Microbiol, 70, 5810, 10.1128/AEM.70.10.5810-5817.2004
Dal Co, 2020, Short-range interactions govern the dynamics and functions of microbial communities, Nat Ecol Evol, 4, 366, 10.1038/s41559-019-1080-2
van Tatenhove-Pel, 2021, Microbial competition reduces metabolic interaction distances to the low µm-range, ISME J, 15, 688, 10.1038/s41396-020-00806-9
Cremer, 2016, Effect of flow and peristaltic mixing on bacterial growth in a gut-like channel, Proc Natl Acad Sci USA, 113, 11414, 10.1073/pnas.1601306113
Kaczmarek, 2017, Complex interactions of circadian rhythms, eating behaviors, and the gastrointestinal microbiota and their potential impact on health, Nutr Rev, 75, 673, 10.1093/nutrit/nux036
Matenchuk, 2020, Sleep, circadian rhythm, and gut microbiota, Sleep Med Rev, 53, 10.1016/j.smrv.2020.101340
Zarrinpar, 2014, Diet and feeding pattern affect the diurnal dynamics of the gut microbiome, Cell Metab, 20, 1006, 10.1016/j.cmet.2014.11.008
Thaiss, 2014, Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis, Cell, 159, 514, 10.1016/j.cell.2014.09.048
Leone, 2015, Effects of diurnal variation of gut microbes and high-fat feeding on host circadian clock function and metabolism, Cell Host Microbe, 17, 681, 10.1016/j.chom.2015.03.006
Geng, 2021, CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention, Proc Natl Acad Sci, 118, 10.1073/pnas.2019336118
Silverman, 2018, Dynamic linear models guide design and analysis of microbiota studies within artificial human guts, Microbiome, 6, 202, 10.1186/s40168-018-0584-3
McNally, 2018, Metabolic model-based analysis of the emergence of bacterial cross-feeding via extensive gene loss, BMC Syst Biol, 12, 69, 10.1186/s12918-018-0588-4
Zelezniak, 2015, Metabolic dependencies drive species co-occurrence in diverse microbial communities, Proc Natl Acad Sci USA, 112, 6449, 10.1073/pnas.1421834112
Tian, 2020, Deciphering functional redundancy in the human microbiome, Nat Commun, 11, 10.1038/s41467-020-19940-1
Paine, 1969, A note on trophic complexity and community stability, Am Nat, 103, 91, 10.1086/282586
Wang, 2019, Evidence for a multi-level trophic organization of the human gut microbiome, PLOS Comput Biol, 15, e1007524, 10.1371/journal.pcbi.1007524
Antonella, 2021, A diverse community to study communities: integration of experiments and mathematical models to study microbial consortia, J Bacteriol, 199, e00865
Bosi, 2017, Perspectives and challenges in microbial communities metabolic modeling, Front Genet, 8, 88, 10.3389/fgene.2017.00088
Perez-Garcia, 2016, Metabolic network modeling of microbial interactions in natural and engineered environmental systems, Front Microbiol, 7, 673, 10.3389/fmicb.2016.00673
Santibáñez, 2019, A tool for statistical and multi-objective calibration of Rule-based models, Sci Rep, 9, 10.1038/s41598-019-51546-6
Qian, 2021, Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models, Curr Opin Microbiol, 62, 84, 10.1016/j.mib.2021.05.003
Venturelli, 2018, Deciphering microbial interactions in synthetic human gut microbiome communities, Mol Syst Biol, 14, 10.15252/msb.20178157
Friedman, 2017, Community structure follows simple assembly rules in microbial microcosms, Nat Ecol Evol, 1, 109, 10.1038/s41559-017-0109
Pinto, 2017, Modeling metabolic interactions in a consortium of the infant gut microbiome, Front Microbiol, 8, 1, 10.3389/fmicb.2017.02507
Sacher, 2011, Improved calibration of a solid substrate fermentation model, Electron J Biotechnol, 14, 7
Julien-Laferrière, 2016, A combinatorial algorithm for microbial consortia synthetic design, Sci Rep, 6, 10.1038/srep29182
Thiele, 2010, A protocol for generating a high-quality genome-scale metabolic reconstruction, Nat Protoc, 5, 93, 10.1038/nprot.2009.203
Henry, 2010, High-throughput generation, optimization and analysis of genome-scale metabolic models, Nat Biotechnol, 28, 977, 10.1038/nbt.1672
Wang, 2018, RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor, PLOS Comput Biol, 14, e1006541, 10.1371/journal.pcbi.1006541
Dias, 2015, Reconstructing genome-scale metabolic models with merlin, Nucleic Acids Res, 43, 3899, 10.1093/nar/gkv294
Kanehisa, 2016, KEGG as a reference resource for gene and protein annotation, Nucleic Acids Res, 44, D457, 10.1093/nar/gkv1070
Caspi, 2016, The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases, Nucleic Acids Res, 44, D471, 10.1093/nar/gkv1164
Thiele, 2014, fastGapFill: efficient gap filling in metabolic networks, Bioinformatics, 30, 2529, 10.1093/bioinformatics/btu321
Zimmermann, 2021, gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models, Genome Biol, 22, 81, 10.1186/s13059-021-02295-1
Machado, 2018, Fast automated reconstruction of genome-scale metabolic models for microbial species and communities, Nucleic Acids Res, 46, 7542, 10.1093/nar/gky537
Aite, 2018, Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models, PLOS Comput Biol, 14, e1006146, 10.1371/journal.pcbi.1006146
Belcour, 2020, Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species, Elife, 9, 10.7554/eLife.61968
Heinken, 2020, AGORA2: Large scale reconstruction of the microbiome highlights wide-spread drug-metabolising capacities, BioRxiv
Heinken, 2021, DEMETER: efficient simultaneous curation of genome-scale reconstructions guided by experimental data and refined gene annotations, Bioinformatics, 37, 3974, 10.1093/bioinformatics/btab622
Heinken, 2019, Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease, Microbiome, 7, 75, 10.1186/s40168-019-0689-3
Heinken, 2013, Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut, Gut Microbes, 4, 28, 10.4161/gmic.22370
Heinken, 2015, Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework, Gut Microbes, 6, 120, 10.1080/19490976.2015.1023494
Lim, 2020, Large-scale metabolic interaction network of the mouse and human gut microbiota, Sci Data, 7
Sen, 2019, Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview, Metabolites, 9, 22, 10.3390/metabo9020022
Orth, 2010, What is flux balance analysis?, Nat Biotechnol, 28, 245, 10.1038/nbt.1614
Price, 2004, Genome-scale models of microbial cells: evaluating the consequences of constraints, Nat Rev Microbiol, 2, 886, 10.1038/nrmicro1023
Gu, 2019, Current status and applications of genome-scale metabolic models, Genome Biol, 20, 121, 10.1186/s13059-019-1730-3
Thiele, 2020, Personalized whole-body models integrate metabolism, physiology, and the gut microbiome, Mol Syst Biol, 16, 10.15252/msb.20198982
Baloni, 2020, Metabolic network analysis reveals altered bile acid synthesis and metabolism in Alzheimeŕs Disease, Cell Reports Med, 1, 100138, 10.1016/j.xcrm.2020.100138
Pacheco, 2019, Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems, Nat Commun, 10, 103, 10.1038/s41467-018-07946-9
Mahadevan, 2003, The effects of alternate optimal solutions in constraint-based genome-scale metabolic models, Metab Eng, 5, 264, 10.1016/j.ymben.2003.09.002
Mahadevan, 2002, Dynamic flux balance analysis of diauxic growth in Escherichia coli, Biophys J, 83, 1331, 10.1016/S0006-3495(02)73903-9
Abdel-Haleem, 2020, Integrated Metabolic Modeling, Culturing, and Transcriptomics Explain Enhanced Virulence of Vibrio cholerae during Coinfection with Enterotoxigenic Escherichia coli, MSystems, 5, 10.1128/mSystems.00491-20
Khandelwal, 2013, Community Flux Balance Analysis for Microbial Consortia at Balanced Growth, PLoS ONE, 8, e64567, 10.1371/journal.pone.0064567
Chan, 2017, Predicting microbial abundances while ensuring community stability, PLoS Comput Biol, 13, e1005539, 10.1371/journal.pcbi.1005539
Chan, 2019, Predicting the Longitudinally and Radially Varying Gut Microbiota Composition using Multi-Scale Microbial Metabolic Modeling, Processes, 7, 10.3390/pr7070394
Harcombe, 2014, Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics, Cell Rep, 7, 1104, 10.1016/j.celrep.2014.03.070
Bauer, 2017, BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities, PLOS Comput Biol, 13, e1005544, 10.1371/journal.pcbi.1005544
Rinninella, 2019, What is the Healthy Gut Microbiota Composition? A Changing Ecosystem across Age, Environment, Diet, and Diseases, Microorganisms, 7, 14, 10.3390/microorganisms7010014
Kunasegaran, 2021, The Modulation of Gut Microbiota Composition in the Pathophysiology of Gestational Diabetes Mellitus: A Systematic Review, Biol, 10, 1027, 10.3390/biology10101027
Sultan, 2021, Metabolic Influences of Gut Microbiota Dysbiosis on Inflammatory Bowel Disease, Front Physiol, 12, 1489, 10.3389/fphys.2021.715506
Clapp, 2017, Gut microbiota’s effect on mental health: The gut-brain axis, Clin Pract, 7, 131, 10.4081/cp.2017.987
Nishida, 2018, Gut microbiota in the pathogenesis of inflammatory bowel disease, Clin J Gastroenterol, 11, 1, 10.1007/s12328-017-0813-5
Diener, 2020, MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota, MSystems, 5, 10.1128/mSystems.00606-19
Hu, 2002, Dietary pattern analysis: a new direction in nutritional epidemiology, Curr Opin Lipidol, 13, 3, 10.1097/00041433-200202000-00002
Foster, 2012, Competition, not cooperation, dominates interactions among culturable microbial species, Curr Biol, 22, 1845, 10.1016/j.cub.2012.08.005
Descheemaeker, 2020, Stochastic logistic models reproduce experimental time series of microbial communities, Elife, 9, 10.7554/eLife.55650
Vega, 2017, Stochastic assembly produces heterogeneous communities in the Caenorhabditis elegans intestine, PLOS Biol, 15, e2000633, 10.1371/journal.pbio.2000633
Shashkova, 2016, Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations, PLoS ONE, 11, e0148386, 10.1371/journal.pone.0148386
Lin, 2018, GutLogo: Agent-based modeling framework to investigate spatial and temporal dynamics in the gut microbiome, PLoS ONE, 13, e0207072, 10.1371/journal.pone.0207072
Ibrahim, 2021, Modelling microbial communities: Harnessing consortia for biotechnological applications, Comput Struct Biotechnol J, 19, 3892, 10.1016/j.csbj.2021.06.048
Buetti-Dinh, 2020, Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations, BMC Bioinf, 21, 10.1186/s12859-019-3337-9
Pfeiffer, 1999, METATOOL: For studying metabolic networks, Bioinformatics, 15, 251, 10.1093/bioinformatics/15.3.251
Arabameri, 2020, Detection of colorectal carcinoma based on microbiota analysis using generalized regression neural networks and nonlinear feature selection, IEEE/ACM Trans Comput Biol Bioinforma, 17, 547
Fukui, 2020, Usefulness of machine learning-based gut microbiome analysis for identifying patients with irritable bowels syndrome, J Clin Med, 9, 2403, 10.3390/jcm9082403
Flynn, 2018, Spatial variation of the native colon microbiota in healthy adults, Cancer Prev Res, 11, 393, 10.1158/1940-6207.CAPR-17-0370
Zampieri, 2019, Machine and deep learning meet genome-scale metabolic modeling, PLOS Comput Biol, 15, e1007084, 10.1371/journal.pcbi.1007084
Walsh, 2021, DOME: recommendations for supervised machine learning validation in biology, Nat Methods, 18, 1122, 10.1038/s41592-021-01205-4
Faust, 2012, Microbial Co-occurrence Relationships in the Human Microbiome, PLOS Comput Biol, 8, e1002606, 10.1371/journal.pcbi.1002606
Chen, 2021, The IMG/M data management and analysis system vol 6.0: new tools and advanced capabilities, Nucleic Acids Res, 49, D751, 10.1093/nar/gkaa939
Methé, 2012, A framework for human microbiome research, Nature, 486, 215, 10.1038/nature11209
Vanderhaeghen, 2015, Methanogen communities in stools of humans of different age and health status and co-occurrence with bacteria, FEMS Microbiol Lett, 362, fnv092, 10.1093/femsle/fnv092
Warren, 2013, Co-occurrence of anaerobic bacteria in colorectal carcinomas, Microbiome, 1, 10.1186/2049-2618-1-16
Thingholm, 2019, Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition, Cell Host Microbe, 26, 252, 10.1016/j.chom.2019.07.004
Baldassano, 2016, Topological distortion and reorganized modular structure of gut microbial co-occurrence networks in inflammatory bowel disease, Sci Rep, 6, 26087, 10.1038/srep26087
Kuenzig, 2018, Co-occurrence of asthma and the inflammatory bowel diseases: A systematic review and meta-analysis, Clin Transl Gastroenterol, 9
Maini Rekdal, 2019, Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism, Science, 364, 10.1126/science.aau6323
Ghannam, 2021, Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring, Comput Struct Biotechnol J, 19, 1092, 10.1016/j.csbj.2021.01.028
Garza, 2018, Towards predicting the environmental metabolome from metagenomics with a mechanistic model, Nat Microbiol, 3, 456, 10.1038/s41564-018-0124-8
Lugli, 2019, Unveiling genomic diversity among members of the species bifidobacterium pseudolongum, a widely distributed gut commensal of the animal kingdom, Appl Environ Microbiol, 85, 10.1128/AEM.03065-18
Lieven, 2020, MEMOTE for standardized genome-scale metabolic model testing, Nat Biotechnol, 38, 272, 10.1038/s41587-020-0446-y
Honda, 2016, The microbiota in adaptive immune homeostasis and disease, Nature, 535, 75, 10.1038/nature18848
Gardner, 2019, Multiscale Multiobjective Systems Analysis (MiMoSA): an advanced metabolic modeling framework for complex systems, Sci Rep, 9, 10.1038/s41598-019-53188-0
Borer, 2019, Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH), PLOS Comput Biol, 15, e1007127, 10.1371/journal.pcbi.1007127
Dukovski, 2021, A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS), Nat Protoc, 16, 5030, 10.1038/s41596-021-00593-3