dc.contributorQuiñones Paredes, Andrés Eduardo
dc.contributorChaib de Mares, Maryam
dc.contributorReyes Muñoz, Alejandro
dc.contributorSantos Vega, Mauricio
dc.contributorCoyte, Katharine
dc.contributorGrupo de Investigación en Biología Computacional y Ecología Microbiana (BCEM)
dc.creatorCastellanos Sánchez, Alejandro
dc.date.accessioned2023-08-01T21:26:48Z
dc.date.accessioned2023-09-07T01:05:39Z
dc.date.available2023-08-01T21:26:48Z
dc.date.available2023-09-07T01:05:39Z
dc.date.created2023-08-01T21:26:48Z
dc.date.issued2023-06-02
dc.identifierhttp://hdl.handle.net/1992/69036
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8728095
dc.description.abstractThe human gut microbiota is composed of complex communities of microorganisms, which are modulated by various factors including diet. It plays a fundamental role in human health and nutrition by metabolizing compounds that are not digestible by the human intestine. Understanding the mechanisms that regulate and modify the microbiota's structure under specific conditions is therefore essential. In this study, we used ecological dynamic modeling grounded on the generalized Lotka-Volterra (gLV) model, and comparative and functional genomics with time-course compositional and transcriptomic data of a representative human gut microbial community inoculated in two groups of gnotobiotic mice subjected to different dietary schemes; with the aim of determining the ecological interactions of the community species, the metabolic functions that mediate these interactions, and how diet influences these interactions. We found that in circumstances where bacteria are growing optimally, the community is enriched with negative interactions, particularly interspecies competition; whereas in circumstances where the community is not growing optimally, fewer interactions can be seen overall. Additionally, the order in which the bacteria are exposed to different diets influence the impact that the diet switch has over the species. Furthermore, bacteria belonging to the Bacteroidetes phylum, which in general dominated the system, present a generalist metabolism, showing a wide repertoire of mechanisms to digest both carbohydrates and amino acid from the diet; while bacteria belonging to other phyla, proved to be specialists, with more reduced and specific metabolic activities to metabolize certain components of the diet, in particular, amino acids. Overall, we present an ecological and functional modeling approach that elucidates relevant mechanisms of gut microbiota structure and dynamics that are hardly detectable using traditional methods of metagenomic analysis.
dc.languageeng
dc.publisherUniversidad de los Andes
dc.publisherMaestría en Biología Computacional
dc.publisherFacultad de Ciencias
dc.publisherDepartamento de Ciencias Biológicas
dc.relationAbreu, C. I., Friedman, J., Andersen Woltz, V. L., & Gore, J. (2019). Mortality causes universal changes in microbial community composition. Nature Communications, 10(1), Article 1. https://doi.org/10.1038/s41467-019-09925-0
dc.relationAusland, C., Zheng, J., Yi, H., Yang, B., Li, T., Feng, X., Zheng, B., & Yin, Y. (2021). dbCAN-PUL: A database of experimentally characterized CAZyme gene clusters and their substrates. Nucleic Acids Research, 49(D1), D523-D528. https://doi.org/10.1093/nar/gkaa742
dc.relationBauer, E., Laczny, C. C., Magnusdottir, S., Wilmes, P., & Thiele, I. (2015). Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires. Microbiome, 3(1), 55. https://doi.org/10.1186/s40168-015-0121-6
dc.relationBauer, E., & Thiele, I. (2018). From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota. MSystems, 3(3), e00209-17. https://doi.org/10.1128/mSystems.00209-17
dc.relationBernstein, D. B., Dewhirst, F. E., & Segrè, D. (2019). Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome. ELife, 8, e39733. https://doi.org/10.7554/eLife.39733
dc.relationBisanz, J. E., Upadhyay, V., Turnbaugh, J. A., Ly, K., & Turnbaugh, P. J. (2019). Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet. Cell Host & Microbe, 26(2), 265-272.e4. https://doi.org/10.1016/j.chom.2019.06.013
dc.relationBucci, V., & Xavier, J. B. (2014). Towards Predictive Models of the Human Gut Microbiome. Journal of Molecular Biology, 426(23), 3907-3916. https://doi.org/10.1016/j.jmb.2014.03.017
dc.relationCantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P., & Huerta-Cepas, J. (2021). eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale (p. 2021.06.03.446934). https://doi.org/10.1101/2021.06.03.446934
dc.relationCao, Y., Wang, Y., Zheng, X., Li, F., & Bo, X. (2016). RevEcoR: An R package for the reverse ecology analysis of microbiomes. BMC Bioinformatics, 17(1), 294. https://doi.org/10.1186/s12859-016-1088-4
dc.relationClark, R. L., Connors, B. M., Stevenson, D. M., Hromada, S. E., Hamilton, J. J., Amador-Noguez, D., & Venturelli, O. S. (2021). Design of synthetic human gut microbiome assembly and butyrate production. Nature Communications, 12(1), Article 1. https://doi.org/10.1038/s41467-021-22938-y
dc.relationConnors, B. M., Ertmer, S., Clark, R. L., Thompson, J., Pfleger, B. F., & Venturelli, O. S. (2022). Model-guided design of the diversity of a synthetic human gut community (p. 2022.03.14.484355). bioRxiv. https://doi.org/10.1101/2022.03.14.484355
dc.relationCoyte, K. Z., & Rakoff-Nahoum, S. (2019). Understanding Competition and Cooperation within the Mammalian Gut Microbiome. Current Biology, 29(11), R538-R544. https://doi.org/10.1016/j.cub.2019.04.017
dc.relationCoyte, K. Z., Rao, C., Rakoff-Nahoum, S., & Foster, K. R. (2021). Ecological rules for the assembly of microbiome communities. PLOS Biology, 19(2), e3001116. https://doi.org/10.1371/journal.pbio.3001116
dc.relationCoyte, K. Z., Schluter, J., & Foster, K. R. (2015). The ecology of the microbiome: Networks, competition, and stability. Science, 350(6261), 663-666. https://doi.org/10.1126/science.aad2602
dc.relationCreswell, R., Tan, J., Leff, J. W., Brooks, B., Mahowald, M. A., Thieroff-Ekerdt, R., & Gerber, G. K. (2020). High-resolution temporal profiling of the human gut microbiome reveals consistent and cascading alterations in response to dietary glycans. Genome Medicine, 12(1), 59. https://doi.org/10.1186/s13073-020-00758-x
dc.relationDiener, C., Gibbons, S. M., & Resendis-Antonio, O. (2020). MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota. MSystems, 5(1), e00606-19. https://doi.org/10.1128/mSystems.00606-19
dc.relationDukovski, I., Bajic, D., Chacón, J. M., Quintin, M., Vila, J. C. C., Sulheim, S., Pacheco, A. R., Bernstein, D. B., Riehl, W. J., Korolev, K. S., Sanchez, A., Harcombe, W. R., & Segrè, D. (2021). A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nature Protocols, 16(11), Article 11. https://doi.org/10.1038/s41596-021-00593-3
dc.relationEmms, D. M., & Kelly, S. (2019). OrthoFinder: Phylogenetic orthology inference for comparative genomics. Genome Biology, 20(1), 238. https://doi.org/10.1186/s13059-019-1832-y
dc.relationEng, A., & Borenstein, E. (2019). Microbial community design: Methods, applications, and opportunities. Current Opinion in Biotechnology, 58, 117-128. https://doi.org/10.1016/j.copbio.2019.03.002
dc.relationFaith, J. J., McNulty, N. P., Rey, F. E., & Gordon, J. I. (2011). Predicting a Human Gut Microbiota's Response to Diet in Gnotobiotic Mice. Science, 333(6038), 101-104. https://doi.org/10.1126/science.1206025
dc.relationFaust, K., & Raes, J. (2012). Microbial interactions: From networks to models. Nature Reviews Microbiology, 10(8), Article 8. https://doi.org/10.1038/nrmicro2832
dc.relationFeng, J., Qian, Y., Zhou, Z., Ertmer, S., Vivas, E. I., Lan, F., Hamilton, J. J., Rey, F. E., Anantharaman, K., & Venturelli, O. S. (2022). Polysaccharide utilization loci in Bacteroides determine population fitness and community-level interactions. Cell Host & Microbe, 30(2), 200-215.e12. https://doi.org/10.1016/j.chom.2021.12.006
dc.relationFeng, L., Raman, A. S., Hibberd, M. C., Cheng, J., Griffin, N. W., Peng, Y., Leyn, S. A., Rodionov, D. A., Osterman, A. L., & Gordon, J. I. (2020). Identifying determinants of bacterial fitness in a model of human gut microbial succession. Proceedings of the National Academy of Sciences, 117(5), 2622-2633. https://doi.org/10.1073/pnas.1918951117
dc.relationFriedman, J., Higgins, L. M., & Gore, J. (2017). Community structure follows simple assembly rules in microbial microcosms. Nature Ecology & Evolution, 1(5), Article 5. https://doi.org/10.1038/s41559-017-0109
dc.relationGalili, T. (2015). dendextend: An R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics, 31(22), 3718-3720. https://doi.org/10.1093/bioinformatics/btv428
dc.relationGálvez, E. J. C., Iljazovic, A., Amend, L., Lesker, T. R., Renault, T., Thiemann, S., Hao, L., Roy, U., Gronow, A., Charpentier, E., & Strowig, T. (2020). Distinct Polysaccharide Utilization Determines Interspecies Competition between Intestinal Prevotella spp. Cell Host & Microbe, 28(6), 838-852.e6. https://doi.org/10.1016/j.chom.2020.09.012
dc.relationGao, C., Xu, L., Montoya, L., Madera, M., Hollingsworth, J., Chen, L., Purdom, E., Singan, V., Vogel, J., Hutmacher, R. B., Dahlberg, J. A., Coleman-Derr, D., Lemaux, P. G., & Taylor, J. W. (2022). Co-occurrence networks reveal more complexity than community composition in resistance and resilience of microbial communities. Nature Communications, 13(1), Article 1. https://doi.org/10.1038/s41467-022-31343-y
dc.relationGibson, T. E., Kim, Y., Acharya, S., Kaplan, D. E., DiBenedetto, N., Lavin, R., Berger, B., Allegretti, J. R., Bry, L., & Gerber, G. K. (2021). Intrinsic instability of the dysbiotic microbiome revealed through dynamical systems inference at scale (p. 2021.12.14.469105). bioRxiv. https://doi.org/10.1101/2021.12.14.469105
dc.relationGibson, T., & Gerber, G. (2018). Robust and Scalable Models of Microbiome Dynamics. Proceedings of the 35th International Conference on Machine Learning, 1763-1772. https://proceedings.mlr.press/v80/gibson18a.html
dc.relationGomez-Arango, L. F., Barrett, H. L., Wilkinson, S. A., Callaway, L. K., McIntyre, H. D., Morrison, M., & Dekker Nitert, M. (2018). Low dietary fiber intake increases Collinsella abundance in the gut microbiota of overweight and obese pregnant women. Gut Microbes, 9(3), 189-201. https://doi.org/10.1080/19490976.2017.1406584
dc.relationGonze, D., Coyte, K. Z., Lahti, L., & Faust, K. (2018). Microbial communities as dynamical systems. Current Opinion in Microbiology, 44, 41-49. https://doi.org/10.1016/j.mib.2018.07.004
dc.relationHarcombe, W. R., Riehl, W. J., Dukovski, I., Granger, B. R., Betts, A., Lang, A. H., Bonilla, G., Kar, A., Leiby, N., Mehta, P., Marx, C. J., & Segrè, D. (2014). Metabolic Resource Allocation in Individual Microbes Determines Ecosystem Interactions and Spatial Dynamics. Cell Reports, 7(4), 1104-1115. https://doi.org/10.1016/j.celrep.2014.03.070
dc.relationHarris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., ... Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), Article 7825. https://doi.org/10.1038/s41586-020-2649-2
dc.relationHoloViz Team. (2023). HoloViews [Python]. HoloViz. https://github.com/holoviz/holoviews (Original work published 2014)
dc.relationHromada, S., Qian, Y., Jacobson, T. B., Clark, R. L., Watson, L., Safdar, N., Amador-Noguez, D., & Venturelli, O. S. (2021). Negative interactions determine Clostridioides difficile growth in synthetic human gut communities. Molecular Systems Biology, 17(10), e10355. https://doi.org/10.15252/msb.202110355
dc.relationHuerta-Cepas, J., Szklarczyk, D., Heller, D., Hernández-Plaza, A., Forslund, S. K., Cook, H., Mende, D. R., Letunic, I., Rattei, T., Jensen, L. J., von Mering, C., & Bork, P. (2019). eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Research, 47(D1), D309-D314. https://doi.org/10.1093/nar/gky1085
dc.relationHunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90-95. https://doi.org/10.1109/MCSE.2007.55
dc.relationHuttenhower, C., Gevers, D., Knight, R., Abubucker, S., Badger, J. H., Chinwalla, A. T., Creasy, H. H., Earl, A. M., FitzGerald, M. G., Fulton, R. S., Giglio, M. G., Hallsworth-Pepin, K., Lobos, E. A., Madupu, R., Magrini, V., Martin, J. C., Mitreva, M., Muzny, D. M., Sodergren, E. J., ... The Human Microbiome Project Consortium. (2012). Structure, function and diversity of the healthy human microbiome. Nature, 486(7402), Article 7402. https://doi.org/10.1038/nature11234
dc.relationInkscape Teams. (2022). Inkscape (1.2.2). https://inkscape.org/
dc.relationKishore, D., Birzu, G., Hu, Z., DeLisi, C., Korolev, K. S., & Segrè, D. (2020). Inferring microbial co-occurrence networks from amplicon data: A systematic evaluation (p. 2020.09.23.309781). bioRxiv. https://doi.org/10.1101/2020.09.23.309781
dc.relationKumar, M., Ji, B., Zengler, K., & Nielsen, J. (2019). Modelling approaches for studying the microbiome. Nature Microbiology, 4(8), Article 8. https://doi.org/10.1038/s41564-019-0491-9
dc.relationKumar, R., Carroll, C., Hartikainen, A., & Martin, O. (2019). ArviZ a unified library for exploratory analysis of Bayesian models in Python. Journal of Open Source Software, 4(33), 1143. https://doi.org/10.21105/joss.01143
dc.relationLee, M. D. (2019). GToTree: A user-friendly workflow for phylogenomics. Bioinformatics, 35(20), 4162-4164. https://doi.org/10.1093/bioinformatics/btz188
dc.relationLeggieri, P. A., & Venturelli, O. S. (2021). Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes. Annual Review of Biomedical Engineering. https://doi.org/10.1146/annurev-bioeng-082120-022836
dc.relationLevy, R., & Borenstein, E. (2013). Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proceedings of the National Academy of Sciences, 110(31), 12804-12809. https://doi.org/10.1073/pnas.1300926110
dc.relationLex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., & Pfister, H. (2014). UpSet: Visualization of Intersecting Sets. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1983-1992. https://doi.org/10.1109/TVCG.2014.2346248
dc.relationLey, R. E., Peterson, D. A., & Gordon, J. I. (2006). Ecological and Evolutionary Forces Shaping Microbial Diversity in the Human Intestine. Cell, 124(4), 837-848. https://doi.org/10.1016/j.cell.2006.02.017
dc.relationLiu, H., Liao, C., Wu, L., Tang, J., Chen, J., Lei, C., Zheng, L., Zhang, C., Liu, Y.-Y., Xavier, J., & Dai, L. (2022). Ecological dynamics of the gut microbiome in response to dietary fiber. The ISME Journal, 16(8), Article 8. https://doi.org/10.1038/s41396-022-01253-4
dc.relationMazumdar, V., Amar, S., & Segrè, D. (2013). Metabolic Proximity in the Order of Colonization of a Microbial Community. PLOS ONE, 8(10), e77617. https://doi.org/10.1371/journal.pone.0077617
dc.relationMcDonald, D., Hyde, E., Debelius, J. W., Morton, J. T., Gonzalez, A., Ackermann, G., Aksenov, A. A., Behsaz, B., Brennan, C., Chen, Y., Goldasich, L. D., Dorrestein, P. C., Dunn, R. R., Fahimipour, A. K., Gaffney, J., Gilbert, J. A., Gogul, G., Green, J. L., Hugenholtz, P., ... Knight, R. (2018). American Gut: An Open Platform for Citizen Science Microbiome Research. MSystems. https://doi.org/10.1128/mSystems.00031-18
dc.relationMcKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, 56-61. https://doi.org/10.25080/Majora-92bf1922-00a
dc.relationMcKinney, W. (2011). pandas: A Foundational Python Library for Data Analysis and Statistics.
dc.relationMcNulty, N. P., Wu, M., Erickson, A. R., Pan, C., Erickson, B. K., Martens, E. C., Pudlo, N. A., Muegge, B. D., Henrissat, B., Hettich, R. L., & Gordon, J. I. (2013). Effects of Diet on Resource Utilization by a Model Human Gut Microbiota Containing Bacteroides cellulosilyticus WH2, a Symbiont with an Extensive Glycobiome. PLOS Biology, 11(8), e1001637. https://doi.org/10.1371/journal.pbio.1001637
dc.relationMcNulty, N. P., Yatsunenko, T., Hsiao, A., Faith, J. J., Muegge, B. D., Goodman, A. L., Henrissat, B., Oozeer, R., Cools-Portier, S., Gobert, G., Chervaux, C., Knights, D., Lozupone, C. A., Knight, R., Duncan, A. E., Bain, J. R., Muehlbauer, M. J., Newgard, C. B., Heath, A. C., & Gordon, J. I. (2011). The Impact of a Consortium of Fermented Milk Strains on the Gut Microbiome of Gnotobiotic Mice and Monozygotic Twins. Science Translational Medicine, 3(106), 106ra106-106ra106. https://doi.org/10.1126/scitranslmed.3002701
dc.relationNothman, J. (2023). UpSetPlot: Draw Lex et al.'s UpSet plots with Pandas and Matplotlib (0.8.0) [Python]. https://upsetplot.readthedocs.io
dc.relationParadis, E., & Schliep, K. (2019). ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics, 35(3), 526-528. https://doi.org/10.1093/bioinformatics/bty633
dc.relationPark, S.-Y., Rao, C., Coyte, K. Z., Kuziel, G. A., Zhang, Y., Huang, W., Franzosa, E. A., Weng, J.-K., Huttenhower, C., & Rakoff-Nahoum, S. (2022). Strain-level fitness in the gut microbiome is an emergent property of glycans and a single metabolite. Cell, 185(3), 513-529.e21. https://doi.org/10.1016/j.cell.2022.01.002
dc.relationPedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825-2830.
dc.relationPontrelli, S., Szabo, R., Pollak, S., Schwartzman, J., Ledezma-Tejeida, D., Cordero, O. X., & Sauer, U. (2022). Metabolic cross-feeding structures the assembly of polysaccharide degrading communities. Science Advances, 8(8), eabk3076. https://doi.org/10.1126/sciadv.abk3076
dc.relationPudlo, N. A., Urs, K., Crawford, R., Pirani, A., Atherly, T., Jimenez, R., Terrapon, N., Henrissat, B., Peterson, D., Ziemer, C., Snitkin, E., & Martens, E. C. (2022). Phenotypic and Genomic Diversification in Complex Carbohydrate-Degrading Human Gut Bacteria. MSystems, 7(1), e00947-21. https://doi.org/10.1128/msystems.00947-21
dc.relationQian, Y., Lan, F., & Venturelli, O. S. (2021). Towards a deeper understanding of microbial communities: Integrating experimental data with dynamic models. Current Opinion in Microbiology, 62, 84-92. https://doi.org/10.1016/j.mib.2021.05.003
dc.relationRao, C., Coyte, K. Z., Bainter, W., Geha, R. S., Martin, C. R., & Rakoff-Nahoum, S. (2021). Multi-kingdom ecological drivers of microbiota assembly in preterm infants. Nature, 591(7851), Article 7851. https://doi.org/10.1038/s41586-021-03241-8
dc.relationRaschka, S. (2018). MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack. Journal of Open Source Software, 3(24), 638. https://doi.org/10.21105/joss.00638
dc.relationReyes, A., Wu, M., McNulty, N. P., Rohwer, F. L., & Gordon, J. I. (2013). Gnotobiotic mouse model of phage-bacterial host dynamics in the human gut. Proceedings of the National Academy of Sciences, 110(50), 20236-20241. https://doi.org/10.1073/pnas.1319470110
dc.relationRodriguez-Castaño, G. P., Caro-Quintero, A., Reyes, A., & Lizcano, F. (2017). Advances in Gut Microbiome Research, Opening New Strategies to Cope with a Western Lifestyle. Frontiers in Genetics, 7. https://www.frontiersin.org/articles/10.3389/fgene.2016.00224
dc.relationSaifuddin, M., Bhatnagar, J. M., Segrè, D., & Finzi, A. C. (2019). Microbial carbon use efficiency predicted from genome-scale metabolic models. Nature Communications, 10(1), Article 1. https://doi.org/10.1038/s41467-019-11488-z
dc.relationSeedorf, H., Griffin, N. W., Ridaura, V. K., Reyes, A., Cheng, J., Rey, F. E., Smith, M. I., Simon, G. M., Scheffrahn, R. H., Woebken, D., Spormann, A. M., Van Treuren, W., Ursell, L. K., Pirrung, M., Robbins-Pianka, A., Cantarel, B. L., Lombard, V., Henrissat, B., Knight, R., & Gordon, J. I. (2014). Bacteria from Diverse Habitats Colonize and Compete in the Mouse Gut. Cell, 159(2), 253-266. https://doi.org/10.1016/j.cell.2014.09.008
dc.relationSeemann, T. (2014). Prokka: Rapid prokaryotic genome annotation. Bioinformatics, 30(14), 2068-2069. https://doi.org/10.1093/bioinformatics/btu153
dc.relationShannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research, 13(11), 2498-2504. https://doi.org/10.1101/gr.1239303
dc.relationSimpson, H. L., & Campbell, B. J. (2015). Review article: Dietary fibre-microbiota interactions. Alimentary Pharmacology & Therapeutics, 42(2), 158-179. https://doi.org/10.1111/apt.13248
dc.relationStein, R. R., Bucci, V., Toussaint, N. C., Buffie, C. G., Rätsch, G., Pamer, E. G., Sander, C., & Xavier, J. B. (2013). Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota. PLOS Computational Biology, 9(12), e1003388. https://doi.org/10.1371/journal.pcbi.1003388
dc.relationvan den Berg, N. I., Machado, D., Santos, S., Rocha, I., Chacón, J., Harcombe, W., Mitri, S., & Patil, K. R. (2022). Ecological modelling approaches for predicting emergent properties in microbial communities. Nature Ecology & Evolution, 1-11. https://doi.org/10.1038/s41559-022-01746-7
dc.relationVenturelli, O., Carr, A. V., Fisher, G., Hsu, R. H., Lau, R., Bowen, B. P., Hromada, S., Northen, T., & Arkin, A. P. (2018). Deciphering microbial interactions in synthetic human gut microbiome communities. Molecular Systems Biology, 14(6), e8157. https://doi.org/10.15252/msb.20178157
dc.relationVieira-Silva, S., Falony, G., Darzi, Y., Lima-Mendez, G., Garcia Yunta, R., Okuda, S., Vandeputte, D., Valles-Colomer, M., Hildebrand, F., Chaffron, S., & Raes, J. (2016). Species-function relationships shape ecological properties of the human gut microbiome. Nature Microbiology, 1(8), Article 8. https://doi.org/10.1038/nmicrobiol.2016.88
dc.relationVirtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., ... van Mulbregt, P. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), Article 3. https://doi.org/10.1038/s41592-019-0686-2
dc.relationWardman, J. F., Bains, R. K., Rahfeld, P., & Withers, S. G. (2022). Carbohydrate-active enzymes (CAZymes) in the gut microbiome. Nature Reviews Microbiology, 20(9), Article 9. https://doi.org/10.1038/s41579-022-00712-1
dc.relationWaskom, M. L. (2021). seaborn: Statistical data visualization. Journal of Open Source Software, 6(60), 3021. https://doi.org/10.21105/joss.03021
dc.relationZelezniak, A., Andrejev, S., Ponomarova, O., Mende, D. R., Bork, P., & Patil, K. R. (2015). Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proceedings of the National Academy of Sciences, 112(20), 6449-6454. https://doi.org/10.1073/pnas.1421834112
dc.relationZinöcker, M. K., & Lindseth, I. A. (2018). The Western Diet-Microbiome-Host Interaction and Its Role in Metabolic Disease. Nutrients, 10(3), Article 3. https://doi.org/10.3390/nu10030365
dc.rightsAtribución-CompartirIgual 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-sa/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleTowards an Ecological and Functional Framework for Modeling the Structure and Dynamics of the Human Gut Microbiome
dc.typeTrabajo de grado - Maestría


Este ítem pertenece a la siguiente institución