Literature DB >> 22796884

Microbial interactions: from networks to models.

Karoline Faust1, Jeroen Raes.   

Abstract

Metagenomics and 16S pyrosequencing have enabled the study of ecosystem structure and dynamics to great depth and accuracy. Co-occurrence and correlation patterns found in these data sets are increasingly used for the prediction of species interactions in environments ranging from the oceans to the human microbiome. In addition, parallelized co-culture assays and combinatorial labelling experiments allow high-throughput discovery of cooperative and competitive relationships between species. In this Review, we describe how these techniques are opening the way towards global ecosystem network prediction and the development of ecosystem-wide dynamic models.

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Year:  2012        PMID: 22796884     DOI: 10.1038/nrmicro2832

Source DB:  PubMed          Journal:  Nat Rev Microbiol        ISSN: 1740-1526            Impact factor:   60.633


  93 in total

1.  Revealing ecological networks using Bayesian network inference algorithms.

Authors:  Isobel Milns; Colin M Beale; V Anne Smith
Journal:  Ecology       Date:  2010-07       Impact factor: 5.499

2.  Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.

Authors:  T Z DeSantis; P Hugenholtz; N Larsen; M Rojas; E L Brodie; K Keller; T Huber; D Dalevi; P Hu; G L Andersen
Journal:  Appl Environ Microbiol       Date:  2006-07       Impact factor: 4.792

3.  A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics.

Authors:  Juliane Schäfer; Korbinian Strimmer
Journal:  Stat Appl Genet Mol Biol       Date:  2005-11-14

4.  Synthetic ecology: a model system for cooperation.

Authors:  Maitreya J Dunham
Journal:  Proc Natl Acad Sci U S A       Date:  2007-01-31       Impact factor: 11.205

5.  Biological populations with nonoverlapping generations: stable points, stable cycles, and chaos.

Authors:  R M May
Journal:  Science       Date:  1974-11-15       Impact factor: 47.728

6.  Convergent temporal dynamics of the human infant gut microbiota.

Authors:  Pål Trosvik; Nils Christian Stenseth; Knut Rudi
Journal:  ISME J       Date:  2009-08-27       Impact factor: 10.302

Review 7.  Microbial community profiling for human microbiome projects: Tools, techniques, and challenges.

Authors:  Micah Hamady; Rob Knight
Journal:  Genome Res       Date:  2009-04-21       Impact factor: 9.043

8.  Linking long-term dietary patterns with gut microbial enterotypes.

Authors:  Gary D Wu; Jun Chen; Christian Hoffmann; Kyle Bittinger; Ying-Yu Chen; Sue A Keilbaugh; Meenakshi Bewtra; Dan Knights; William A Walters; Rob Knight; Rohini Sinha; Erin Gilroy; Kernika Gupta; Robert Baldassano; Lisa Nessel; Hongzhe Li; Frederic D Bushman; James D Lewis
Journal:  Science       Date:  2011-09-01       Impact factor: 47.728

9.  NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways.

Authors:  Sylvain Brohée; Karoline Faust; Gipsi Lima-Mendez; Olivier Sand; Rekin's Janky; Gilles Vanderstocken; Yves Deville; Jacques van Helden
Journal:  Nucleic Acids Res       Date:  2008-06-04       Impact factor: 16.971

10.  A synthetic Escherichia coli predator-prey ecosystem.

Authors:  Frederick K Balagaddé; Hao Song; Jun Ozaki; Cynthia H Collins; Matthew Barnet; Frances H Arnold; Stephen R Quake; Lingchong You
Journal:  Mol Syst Biol       Date:  2008-04-15       Impact factor: 11.429

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  638 in total

1.  Quantitative Analysis of Lysobacter Predation.

Authors:  Ivana Seccareccia; Christian Kost; Markus Nett
Journal:  Appl Environ Microbiol       Date:  2015-07-31       Impact factor: 4.792

2.  Insights in the ecology and evolutionary history of the Miscellaneous Crenarchaeotic Group lineage.

Authors:  Mireia Fillol; Jean-Christophe Auguet; Emilio O Casamayor; Carles M Borrego
Journal:  ISME J       Date:  2015-08-18       Impact factor: 10.302

3.  Bacterial predation in a marine host-associated microbiome.

Authors:  Rory M Welsh; Jesse R Zaneveld; Stephanie M Rosales; Jérôme P Payet; Deron E Burkepile; Rebecca Vega Thurber
Journal:  ISME J       Date:  2015-11-27       Impact factor: 10.302

4.  Algorithms for modeling global and context-specific functional relationship networks.

Authors:  Fan Zhu; Bharat Panwar; Yuanfang Guan
Journal:  Brief Bioinform       Date:  2015-08-06       Impact factor: 11.622

5.  Divergent Co-occurrence Patterns and Assembly Processes Structure the Abundant and Rare Bacterial Communities in a Salt Marsh Ecosystem.

Authors:  Shicong Du; Francisco Dini-Andreote; Nan Zhang; Chunling Liang; Zhiyuan Yao; Huajun Zhang; Demin Zhang
Journal:  Appl Environ Microbiol       Date:  2020-06-17       Impact factor: 4.792

6.  Interstrain interactions between bacteria isolated from vacuum-packaged refrigerated beef.

Authors:  Peipei Zhang; József Baranyi; Mark Tamplin
Journal:  Appl Environ Microbiol       Date:  2015-02-06       Impact factor: 4.792

7.  Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations.

Authors:  Johanna W Lampe; Sandi L Navarro; Meredith A J Hullar; Ali Shojaie
Journal:  Proc Nutr Soc       Date:  2013-02-06       Impact factor: 6.297

Review 8.  Ammonia-oxidizing archaea in biological interactions.

Authors:  Jong-Geol Kim; Khaled S Gazi; Samuel Imisi Awala; Man-Young Jung; Sung-Keun Rhee
Journal:  J Microbiol       Date:  2021-02-23       Impact factor: 3.422

9.  Gut microbiota dysbiosis is associated with malnutrition and reduced plasma amino acid levels: Lessons from genome-scale metabolic modeling.

Authors:  Manish Kumar; Boyang Ji; Parizad Babaei; Promi Das; Dimitra Lappa; Girija Ramakrishnan; Todd E Fox; Rashidul Haque; William A Petri; Fredrik Bäckhed; Jens Nielsen
Journal:  Metab Eng       Date:  2018-07-31       Impact factor: 9.783

10.  The Underlying Ecological Processes of Gut Microbiota Among Cohabitating Retarded, Overgrown and Normal Shrimp.

Authors:  Jinbo Xiong; Wenfang Dai; Jinyong Zhu; Keshao Liu; Chunming Dong; Qiongfen Qiu
Journal:  Microb Ecol       Date:  2016-12-13       Impact factor: 4.552

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