Modeling microbial communities: current, developing, and future technologies for predicting microbial community interaction.

Never has there been a greater opportunity for investigating microbial communities. Not only are the profound effects of microbial ecology on every aspect of Earth's geochemical cycles beginning to be understood, but also the analytical and computational tools for investigating microbial Earth are undergoing a rapid revolution. This environmental microbial interactome, the system of interactions between the microbiome and the environment, has shaped the planet's past and will undoubtedly continue to do so in the future. We review recent approaches for modeling microbial community structures and the interactions of microbial populations with their environments. Different modeling approaches consider the environmental microbial interactome from different aspects, and each provides insights to different facets of microbial ecology. We discuss the challenges and opportunities for the future of microbial modeling and describe recent advances in microbial community modeling that are extending current descriptive technologies into a predictive science.

[1]  M. Böttcher,et al.  Experimental investigation of sulphur isotope partitioning during outgassing of hydrogen sulphide from diluted aqueous solutions and seawater , 2010, Isotopes in environmental and health studies.

[2]  C. Margules,et al.  Nature Conservation: Cost Effective Biological Surveys and Data Analysis , 1990 .

[3]  E. Klumpp,et al.  Enumeration of soil bacteria with the green fluorescent nucleic acid dye Sytox green in the presence of soil particles. , 2004, Journal of microbiological methods.

[4]  Mark R Viant,et al.  Recent developments in environmental metabolomics. , 2008, Molecular bioSystems.

[5]  Khaled Rasheed,et al.  Decision tree and ensemble learning algorithms with their applications in bioinformatics. , 2011, Advances in experimental medicine and biology.

[6]  J. Whitfield Is Everything Everywhere? , 2005, Science.

[7]  Rick L. Stevens,et al.  High-throughput generation, optimization and analysis of genome-scale metabolic models , 2010, Nature Biotechnology.

[8]  C. Pedrós-Alió,et al.  Marine microbial diversity: can it be determined? , 2006, Trends in microbiology.

[9]  Ganga M. Hettiarachchi,et al.  Distribution and speciation of nutrient elements around micropores. , 2009 .

[10]  Noha H. Youssef,et al.  Novelty and Uniqueness Patterns of Rare Members of the Soil Biosphere , 2008, Applied and Environmental Microbiology.

[11]  O. Hoegh‐Guldberg Dangerous shifts in ocean ecosystem function? , 2010, The ISME Journal.

[12]  Edward Vanden Berghe,et al.  A one ocean model of biodiversity , 2009 .

[13]  John W. Crawford,et al.  Determination of soil hydraulic conductivity with the lattice Boltzmann method and soil thin-section technique , 2005 .

[14]  J. Whitfield Biogeography. Is everything everywhere? , 2005, Science.

[15]  M. Luoto,et al.  Biotic interactions improve prediction of boreal bird distributions at macro‐scales , 2007 .

[16]  R. Daniel,et al.  Achievements and new knowledge unraveled by metagenomic approaches , 2009, Applied Microbiology and Biotechnology.

[17]  John C. Wooley,et al.  A Primer on Metagenomics , 2010, PLoS Comput. Biol..

[18]  Patrick D. Schloss,et al.  Assessing and Improving Methods Used in Operational Taxonomic Unit-Based Approaches for 16S rRNA Gene Sequence Analysis , 2011, Applied and Environmental Microbiology.

[19]  L. Perelman,et al.  A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers , 1997 .

[20]  Sebastiaan A.L.M. Kooijman,et al.  A biodiversity‐inspired approach to aquatic ecosystem modeling , 2007 .

[21]  S. Islam,et al.  Warming oceans, phytoplankton, and river discharge: implications for cholera outbreaks. , 2011, The American journal of tropical medicine and hygiene.

[22]  J. Tiedje,et al.  Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy , 2007, Applied and Environmental Microbiology.

[23]  R. Dean Graetz,et al.  Remote Sensing of Terrestrial Ecosystem Structure: An Ecologist’s Pragmatic View , 1990 .

[24]  J. Rine,et al.  Serial Analysis of rRNA Genes and the Unexpected Dominance of Rare Members of Microbial Communities , 2007, Applied and Environmental Microbiology.

[25]  Adam Godzik,et al.  Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences , 2006, Bioinform..

[26]  William A. Walters,et al.  QIIME allows analysis of high-throughput community sequencing data , 2010, Nature Methods.

[27]  W. Whitman,et al.  Prokaryotes: the unseen majority. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Kenneth H. Nealson,et al.  Mineral surfaces and their implications for microbial attachment: Results from Monte Carlo simulations and direct surface observations , 2003 .

[29]  S. Tringe,et al.  Metagenomic Discovery of Biomass-Degrading Genes and Genomes from Cow Rumen , 2011, Science.

[30]  N. Ostle,et al.  Microbial contributions to climate change through carbon cycle feedbacks , 2008, The ISME Journal.

[31]  M. Aragno,et al.  Bacterial diversity in the bulk soil and rhizosphere fractions of Lolium perenne and Trifolium repens as revealed by PCR restriction analysis of 16S rDNA , 2004, Plant and Soil.

[32]  Kevin P. Keegan,et al.  Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset , 2011, Microbial Informatics and Experimentation.

[33]  Susan M. Huse,et al.  The Taxonomic and Functional Diversity of Microbes at a Temperate Coastal Site: A ‘Multi-Omic’ Study of Seasonal and Diel Temporal Variation , 2010, PloS one.

[34]  Journal of Biotechnology , 2022 .

[35]  Mollie E. Brooks,et al.  Generalized linear mixed models: a practical guide for ecology and evolution. , 2009, Trends in ecology & evolution.

[36]  D. Antonopoulos,et al.  Using the metagenomics RAST server (MG-RAST) for analyzing shotgun metagenomes. , 2010, Cold Spring Harbor protocols.

[37]  Clara Prats,et al.  Individual-based Modelling: An Essential Tool for Microbiology , 2008, Journal of biological physics.

[38]  F. Glöckner,et al.  Marine microbial genomics in Europe: current status and perspectives , 2010, Microbial biotechnology.

[39]  David R. B. Stockwell,et al.  Induction of sets of rules from animal distribution data: a robust and informative method of data analysis , 1992 .

[40]  Nicholas R. Bates,et al.  Pelagic functional group modeling: Progress, challenges and prospects , 2006 .

[41]  S. Doney,et al.  From genes to ecosystems: the ocean's new frontier , 2004 .

[42]  G. Carpenter,et al.  DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals , 1993, Biodiversity & Conservation.

[43]  M. Aragno,et al.  Phylogenetic diversity of bacterial communities differing in degree of proximity of Lolium perenne and Trifolium repens roots , 1999 .

[44]  C. Mobley,et al.  Optical remote sensing techniques in biological oceanography , 2001 .

[45]  Thomas R. Anderson,et al.  Parameter optimisation techniques and the problem of underdetermination in marine biogeochemical models , 2010 .

[46]  G. Cochrane,et al.  The Genomic Standards Consortium , 2011, PLoS biology.

[47]  M. V. D. van der Heijden,et al.  The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. , 2008, Ecology letters.

[48]  V. Kunin,et al.  Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates. , 2009, Environmental microbiology.

[49]  Paul G Dennis,et al.  Are root exudates more important than other sources of rhizodeposits in structuring rhizosphere bacterial communities? , 2010, FEMS microbiology ecology.

[50]  Jason Feldman,et al.  Remote Detection of Marine Microbes, Small Invertebrates, Harmful Algae, and Biotoxins using the Environmental Sample Processor (ESP) , 2009 .

[51]  D. Strayer,et al.  Usefulness of Bioclimatic Models for Studying Climate Change and Invasive Species , 2008, Annals of the New York Academy of Sciences.

[52]  Kai W. Wirtz,et al.  A trait-based approach for downscaling complexity in plankton ecosystem models , 2009 .

[53]  J. Gilbert,et al.  Microbial metagenomics: beyond the genome. , 2011, Annual review of marine science.

[54]  Gary L. Andersen,et al.  High-Density Microarray of Small-Subunit Ribosomal DNA Probes , 2002, Applied and Environmental Microbiology.

[55]  T. Moorman,et al.  Fluorescent In Situ Hybridization and Micro-autoradiography Applied to Ecophysiology in Soil , 2007 .

[56]  Sallie W. Chisholm,et al.  Emergent Biogeography of Microbial Communities in a Model Ocean , 2007, Science.

[57]  D. Griffin,et al.  Atmospheric Movement of Microorganisms in Clouds of Desert Dust and Implications for Human Health , 2007, Clinical Microbiology Reviews.

[58]  D. Caron,et al.  Marine bacterial, archaeal and protistan association networks reveal ecological linkages , 2011, The ISME Journal.

[59]  S. Lavender,et al.  Mapping size-specific phytoplankton primary production on a global scale , 2010 .

[60]  P. Vandamme,et al.  DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. , 2007, International journal of systematic and evolutionary microbiology.

[61]  P. Walker,et al.  HABITAT : a procedure for modelling a disjoint environmental envelope for a plant or animal species , 1991 .

[62]  Jizhong Zhou,et al.  Metagenomic analysis reveals a marked divergence in the structure of belowground microbial communities at elevated CO2. , 2010, Ecology letters.

[63]  I M Young,et al.  Interactions and Self-Organization in the Soil-Microbe Complex , 2004, Science.

[64]  J. Busby BIOCLIM - a bioclimate analysis and prediction system , 1991 .

[65]  V. V. Bulygin,et al.  Examination of Microbial Proteome Preservation Techniques Applicable to Autonomous Environmental Sample Collection , 2011, Front. Microbio..

[66]  Andreas Wilke,et al.  phylogenetic and functional analysis of metagenomes , 2022 .

[67]  Dawn Field,et al.  The seasonal structure of microbial communities in the Western English Channel. , 2009, Environmental microbiology.

[68]  Donghai Zheng,et al.  Evaluation of soil nitrogen emissions from riparian zones coupling simple process-oriented models with remote sensing data. , 2010, The Science of the total environment.

[69]  Lior Pachter,et al.  Bioinformatics for Whole-Genome Shotgun Sequencing of Microbial Communities , 2005, PLoS Comput. Biol..

[70]  Rob Knight,et al.  Short-Term Temporal Variability in Airborne Bacterial and Fungal Populations , 2007, Applied and Environmental Microbiology.

[71]  John A. Breier,et al.  A suspended-particle rosette multi-sampler for discrete biogeochemical sampling in low-particle-density waters , 2009 .

[72]  N. Moran,et al.  Aphid genome expression reveals host–symbiont cooperation in the production of amino acids , 2011, Proceedings of the National Academy of Sciences.

[73]  M. Ginovart,et al.  Individual-Based Modeling of Carbon and Nitrogen Dynamics in Soils: Parameterization and Sensitivity Analysis of Abiotic Components , 2010 .

[74]  J. Banfield,et al.  Community structure and metabolism through reconstruction of microbial genomes from the environment , 2004, Nature.

[75]  K. Konstantinidis,et al.  The bacterial species definition in the genomic era , 2006, Philosophical Transactions of the Royal Society B: Biological Sciences.

[76]  Marta Ginovart,et al.  Individual-based Modelling of microbial activity to study mineralization of C and N and nitrification process in soil , 2005 .

[77]  Philip Hugenholtz,et al.  Impact of Culture-Independent Studies on the Emerging Phylogenetic View of Bacterial Diversity , 1998, Journal of bacteriology.

[78]  J. Gilbert,et al.  Bioturbating shrimp alter the structure and diversity of bacterial communities in coastal marine sediments , 2010, The ISME Journal.

[79]  R. Knight,et al.  The Western English Channel contains a persistent microbial seed bank , 2011, The ISME Journal.

[80]  Jan O. Korbel,et al.  Quantifying environmental adaptation of metabolic pathways in metagenomics , 2009, Proceedings of the National Academy of Sciences.

[81]  H. Tettelin,et al.  The microbial pan-genome. , 2005, Current opinion in genetics & development.

[82]  A. Uitterlinden,et al.  Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA , 1993, Applied and environmental microbiology.

[83]  Rick L. Stevens,et al.  The RAST Server: Rapid Annotations using Subsystems Technology , 2008, BMC Genomics.

[84]  Mark R Viant,et al.  NMR-based metabolomics: a powerful approach for characterizing the effects of environmental stressors on organism health. , 2003, Environmental science & technology.

[85]  Harold A. Mooney,et al.  Remote Sensing of Biosphere Functioning , 1990 .

[86]  Gary L Andersen,et al.  Rapid quantification and taxonomic classification of environmental DNA from both prokaryotic and eukaryotic origins using a microarray. , 2005, FEMS microbiology letters.

[87]  Christine Wiedinmyer,et al.  Characterization of Airborne Microbial Communities at a High-Elevation Site and Their Potential To Act as Atmospheric Ice Nuclei , 2009, Applied and Environmental Microbiology.

[88]  Stephanie Dutkiewicz,et al.  Modeling the coupling of ocean ecology and biogeochemistry , 2009 .

[89]  David R. B. Stockwell,et al.  The GARP modelling system: problems and solutions to automated spatial prediction , 1999, Int. J. Geogr. Inf. Sci..

[90]  R. Tibshirani,et al.  Exploring the nature of covariate effects in the proportional hazards model. , 1990, Biometrics.

[91]  Patrick D. Schloss,et al.  The Effects of Alignment Quality, Distance Calculation Method, Sequence Filtering, and Region on the Analysis of 16S rRNA Gene-Based Studies , 2010, PLoS Comput. Biol..

[92]  Stephanie Dutkiewicz,et al.  Modeling diverse communities of marine microbes. , 2011, Annual review of marine science.

[93]  Marten Scheffer,et al.  Super-individuals a simple solution for modelling large populations on an individual basis , 1995 .

[94]  Susan M. Huse,et al.  Microbial diversity in the deep sea and the underexplored “rare biosphere” , 2006, Proceedings of the National Academy of Sciences.

[95]  John W. Crawford,et al.  Visualization, modelling and prediction in soil microbiology , 2007, Nature Reviews Microbiology.

[96]  M. David,et al.  Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw , 2011, Nature.

[97]  Maureen A. O’Malley The nineteenth century roots of 'everything is everywhere' , 2007, Nature Reviews Microbiology.

[98]  Robert C. Edgar,et al.  BIOINFORMATICS APPLICATIONS NOTE , 2001 .

[99]  Rick L. Stevens,et al.  The Earth Microbiome Project: Meeting report of the “1st EMP meeting on sample selection and acquisition” at Argonne National Laboratory October 6th 2010. , 2010, Standards in genomic sciences.

[100]  A. V. Van Bruggen,et al.  Moving Waves of Bacterial Populations and Total Organic Carbon along Roots of Wheat , 1999, Microbial Ecology.

[101]  J. Crawford,et al.  Spatial distribution of bacterial communities and their relationships with the micro-architecture of soil. , 2003, FEMS microbiology ecology.