Metabolic modeling of synthetic microbial communities for bioremediation

[1]  A. Weisz,et al.  Metabolic Robustness to Growth Temperature of a Cold- Adapted Marine Bacterium , 2023, mSystems.

[2]  P. Xu,et al.  Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use , 2022, mLife.

[3]  L. Ma,et al.  Emerging contaminants antibiotic resistance genes and microplastics in the environment: Introduction to 21 review articles published in CREST during 2018–2022 , 2022, Critical Reviews in Environmental Science and Technology.

[4]  Xihui Xu,et al.  Interspecies Metabolic Interactions in a Synergistic Consortium Drive Efficient Degradation of the Herbicide Bromoxynil Octanoate. , 2022, Journal of agricultural and food chemistry.

[5]  Jing-long Han,et al.  Efficient biodegradation of acetoacetanilide in hypersaline wastewater with a synthetic halotolerant bacterial consortium. , 2022, Journal of hazardous materials.

[6]  S. Campanaro,et al.  Integrating metagenomic binning with flux balance analysis to unravel syntrophies in anaerobic CO2 methanation , 2022, Microbiome.

[7]  Jonathan Friedman,et al.  Interactions between Culturable Bacteria Are Predicted by Individual Species’ Growth , 2022, bioRxiv.

[8]  Song Wang,et al.  Unraveling diverse survival strategies of microorganisms to vanadium stress in aquatic environments. , 2022, Water research.

[9]  E. Ferreira,et al.  merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models , 2022, Nucleic acids research.

[10]  M. Seidl,et al.  Metabolic interactions shape a community's phenotype. , 2022, Trends in microbiology.

[11]  Jianming Xu,et al.  Microbial interactions enhanced environmental fitness and expanded ecological niches under dibutyl phthalate and cadmium co-contamination. , 2022, Environmental pollution.

[12]  Daniel J. Diaz,et al.  Machine learning-aided engineering of hydrolases for PET depolymerization , 2022, Nature.

[13]  R. Geetha,et al.  Persistent organic pollutants in water resources: Fate, occurrence, characterization and risk analysis. , 2022, The Science of the total environment.

[14]  P. Bhatt,et al.  Indigenous bacterial consortium-mediated cypermethrin degradation in the presence of organic amendments and Zea mays plants. , 2022, Environmental research.

[15]  K. Zengler,et al.  Flux balance analysis of the ammonia-oxidizing bacterium Nitrosomonas europaea ATCC19718 unravels specific metabolic activities while degrading toxic compounds , 2022, PLoS Comput. Biol..

[16]  E. Almaas,et al.  Experimental determination of Escherichia coli biomass composition for constraint-based metabolic modeling , 2022, PloS one.

[17]  K. Gowda,et al.  Genomic structure predicts metabolite dynamics in microbial communities , 2020, Cell.

[18]  P. Bhatt,et al.  Biofilm-mediated bioremediation is a powerful tool for the removal of environmental pollutants. , 2022, Chemosphere.

[19]  X. Jia,et al.  Artificial Consortium of Three E. coli BL21 Strains with Synergistic Functional Modules for Complete Phenanthrene Degradation. , 2021, ACS synthetic biology.

[20]  Rosemary Yu,et al.  Adaptations in metabolism and protein translation give rise to the Crabtree effect in yeast , 2021, Proceedings of the National Academy of Sciences.

[21]  B. André,et al.  Overlapping Roles of Yeast Transporters Aqr1, Qdr2, and Qdr3 in Amino Acid Excretion and Cross-Feeding of Lactic Acid Bacteria , 2021, Frontiers in Microbiology.

[22]  Pau Loke Show,et al.  Recent advances biodegradation and biosorption of organic compounds from wastewater: microalgae-bacteria consortium - A Review. , 2021, Bioresource technology.

[23]  D. Purchase,et al.  Quorum sensing-a promising tool for degradation of industrial waste containing persistent organic pollutants. , 2021, Environmental pollution.

[24]  Ting Huang,et al.  Even allocation of benefits stabilizes microbial community engaged in metabolic division of labor , 2021, bioRxiv.

[25]  M. Tsiknia,et al.  Unraveling negative biotic interactions determining soil microbial community assembly and functioning , 2021, The ISME journal.

[26]  Suzanne M. Paley,et al.  The EcoCyc Database in 2021 , 2021, Frontiers in Microbiology.

[27]  Miguel Rocha,et al.  A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale , 2021, bioRxiv.

[28]  Niaz Chowdhury,et al.  Enhancing in silico strain design predictions through next generation metabolic modeling approaches. , 2021, Biotechnology advances.

[29]  Oriol Vinyals,et al.  Highly accurate protein structure prediction with AlphaFold , 2021, Nature.

[30]  A. Basit,et al.  Predicting drug-microbiome interactions with machine learning. , 2021, Biotechnology advances.

[31]  Ophelia S. Venturelli,et al.  Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models. , 2021, Current opinion in microbiology.

[32]  Q. Shen,et al.  Bacillus velezensis stimulates resident rhizosphere Pseudomonas stutzeri for plant health through metabolic interactions , 2021, The ISME Journal.

[33]  Joshua J. Hamilton,et al.  Design of synthetic human gut microbiome assembly and butyrate production , 2021, Nature Communications.

[34]  D. Hogan,et al.  Intraspecies heterogeneity in microbial interactions. , 2021, Current opinion in microbiology.

[35]  S. Vermeire,et al.  Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions , 2021, Frontiers in Microbiology.

[36]  H. Eizenberg,et al.  Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling , 2021, Frontiers in Bioengineering and Biotechnology.

[37]  Zhili He,et al.  Bacteria-driven phthalic acid ester biodegradation: Current status and emerging opportunities. , 2021, Environment international.

[38]  P. Bhatt,et al.  Biotechnological basis of microbial consortia for the removal of pesticides from the environment , 2021, Critical reviews in biotechnology.

[39]  K. Eversole,et al.  Enabling sustainable agriculture through understanding and enhancement of microbiomes. , 2021, The New phytologist.

[40]  E. Almaas,et al.  Addressing uncertainty in genome-scale metabolic model reconstruction and analysis , 2021, Genome Biology.

[41]  Aijie Wang,et al.  Microbial Interactions Drive the Complete Catabolism of the Antibiotic Sulfamethoxazole in Activated Sludge Microbiomes. , 2021, Environmental science & technology.

[42]  Huabao Zheng,et al.  Degradation of tetracycline antibiotics by Arthrobacter nicotianae OTC-16. , 2021, Journal of hazardous materials.

[43]  W. Fagan,et al.  Higher-order effects, continuous species interactions, and trait evolution shape microbial spatial dynamics , 2021, Proceedings of the National Academy of Sciences.

[44]  David A. Drew,et al.  Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals , 2021, Nature Medicine.

[45]  K. Patil,et al.  metaGEM: reconstruction of genome scale metabolic models directly from metagenomes , 2021, bioRxiv.

[46]  E. Almaas,et al.  Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition , 2020, bioRxiv.

[47]  B. Ma,et al.  Network-directed isolation of the cooperator Pseudomonas aeruginosa ZM03 enhanced the dibutyl phthalate degradation capacity of Arthrobacter nicotianae ZM05 under pH stress. , 2020, Journal of hazardous materials.

[48]  Yuanzhi Li,et al.  Advances in higher-order interactions between organisms , 2020 .

[49]  P. McIntyre,et al.  Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika , 2019, The ISME Journal.

[50]  Cory C. Funk,et al.  Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer’s Disease , 2020, Cell reports. Medicine.

[51]  M. Antoniewicz.  A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications. , 2020, Metabolic engineering.

[52]  J. Nielsen,et al.  Proteome constraints reveal targets for improving microbial fitness in nutrient‐rich environments , 2020, bioRxiv.

[53]  E. Zoetendal,et al.  Gut microbiome stability and resilience: elucidating the response to perturbations in order to modulate gut health , 2020, Gut.

[54]  M. Mayfield,et al.  Beyond direct neighbourhood effects: higher-order interactions improve modelling and predicting tree survival and growth , 2020, National science review.

[55]  B. Palsson,et al.  Reconstructing organisms in silico: genome-scale models and their emerging applications , 2020, Nature Reviews Microbiology.

[56]  Jeremy M. Chacón,et al.  A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS) , 2020, Nature Protocols.

[57]  Hyun Uk Kim,et al.  Machine learning applications in systems metabolic engineering. , 2020, Current opinion in biotechnology.

[58]  C. Kost,et al.  Reciprocal Fitness Feedbacks Promote the Evolution of Mutualistic Cooperation , 2020, Current Biology.

[59]  A. Gasbarrini,et al.  Gut microbiome, big data and machine learning to promote precision medicine for cancer , 2020, Nature Reviews Gastroenterology & Hepatology.

[60]  M. Taga,et al.  Sharing vitamins: Cobamides unveil microbial interactions , 2020, Science.

[61]  Chao-hai Wei,et al.  Enhancement of phenol biodegradation: Metabolic division of labor in co-culture of Stenotrophomonas sp. N5 and Advenella sp. B9. , 2020, Journal of hazardous materials.

[62]  Daniel C W Tsang,et al.  Microplastics as pollutants in agricultural soils. , 2020, Environmental pollution.

[63]  E. Elinav,et al.  Interaction between microbiota and immunity in health and disease , 2020, Cell Research.

[64]  D. Or,et al.  The engineering of spatially linked microbial consortia – potential and perspectives , 2020, Current opinion in biotechnology.

[65]  C. Kaleta,et al.  gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models , 2020, Genome Biology.

[66]  Jonathan M. Monk,et al.  Reconstruction and Validation of a Genome-Scale Metabolic Model of Streptococcus oralis (iCJ415), a Human Commensal and Opportunistic Pathogen , 2020, Frontiers in Genetics.

[67]  Ronan M. T. Fleming,et al.  MEMOTE for standardized genome-scale metabolic model testing , 2020, Nature Biotechnology.

[68]  Shaoyong Lu,et al.  Distribution of antibiotic resistance genes in an agriculturally disturbed lake in China: Their links with microbial communities, antibiotics, and water quality. , 2020, Journal of hazardous materials.

[69]  S. Maslov,et al.  Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence , 2020, bioRxiv.

[70]  P. Bork,et al.  Polarization of microbial communities between competitive and cooperative metabolism , 2020, bioRxiv.

[71]  Gregory L Medlock,et al.  Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning , 2019, Cell systems.

[72]  Bernhard O. Palsson,et al.  A workflow for generating multi-strain genome-scale metabolic models of prokaryotes , 2019, Nature Protocols.

[73]  Peter D. Karp,et al.  Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology , 2019, Briefings Bioinform..

[74]  Seppe Kuehn,et al.  Higher-Order Interaction between Species Inhibits Bacterial Invasion of a Phototroph-Predator Microbial Community. , 2019, Cell systems.

[75]  Chunyan Li,et al.  Carboxylesterase, a de-esterification enzyme, catalyzes the degradation of chlorimuron-ethyl in Rhodococcus erythropolis D310-1. , 2019, Journal of hazardous materials.

[76]  Yaojun Tong,et al.  Synthetic biology and metabolic engineering of actinomycetes for natural product discovery. , 2019, Biotechnology advances.

[77]  Anirban Dutta,et al.  Rewiring of Metabolic Network in Mycobacterium tuberculosis During Adaptation to Different Stresses , 2019, Front. Microbiol..

[78]  J. Buck.  Indirect Effects Explain the Role of Parasites in Ecosystems. , 2019, Trends in parasitology.

[79]  Ophelia S. Venturelli,et al.  Common principles and best practices for engineering microbiomes , 2019, Nature Reviews Microbiology.

[80]  Sara Mitri,et al.  Toxicity drives facilitation between 4 bacterial species , 2019, Proceedings of the National Academy of Sciences.

[81]  Claudio Angione,et al.  Machine and deep learning meet genome-scale metabolic modeling , 2019, PLoS Comput. Biol..

[82]  Gi Bae Kim,et al.  Current status and applications of genome-scale metabolic models , 2019, Genome Biology.

[83]  Vassily Hatzimanikatis,et al.  Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH) , 2019, PLoS Comput. Biol..

[84]  Ronan M. T. Fleming,et al.  Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease , 2019, Microbiome.

[85]  Jason A. Papin,et al.  Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments , 2019, bioRxiv.

[86]  Adam M. Feist,et al.  BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data , 2019, PLoS Comput. Biol..

[87]  Chao-hai Wei,et al.  Application of metabolic division of labor in simultaneous removal of nitrogen and thiocyanate from wastewater. , 2019, Water research.

[88]  Anne Richelle,et al.  Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0 , 2019, Nature Protocols.

[89]  L. Alvarez-Cohen,et al.  An integrated meta-omics approach reveals substrates involved in synergistic interactions in a bisphenol A (BPA)-degrading microbial community , 2019, Microbiome.

[90]  E. Almaas,et al.  Automated generation of genome-scale metabolic draft reconstructions based on KEGG , 2018, BMC Bioinformatics.

[91]  Dong-Yup Lee,et al.  Metabolic Modeling of Microbial Community Interactions for Health, Environmental and Biotechnological Applications , 2018, Current genomics.

[92]  H. Eizenberg,et al.  Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions , 2018, The ISME Journal.

[93]  Rick L. Stevens,et al.  KBase: The United States Department of Energy Systems Biology Knowledgebase , 2018, Nature Biotechnology.

[94]  James J Collins,et al.  Designing microbial consortia with defined social interactions , 2018, Nature Chemical Biology.

[95]  B. H. Ousterhout,et al.  Past selection impacts the strength of an aquatic trophic cascade , 2018 .

[96]  Piotr Gawron,et al.  The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease , 2018, bioRxiv.

[97]  Hao Wang,et al.  RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor , 2018, bioRxiv.

[98]  Olivier Dameron,et al.  Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models , 2018, PLoS Comput. Biol..

[99]  Alan R. Pacheco,et al.  Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems , 2018, bioRxiv.

[100]  D. Machado,et al.  Fast automated reconstruction of genome-scale metabolic models for microbial species and communities , 2018, bioRxiv.

[101]  J. Grilli,et al.  Higher-order interactions stabilize dynamics in competitive network models , 2017, Nature.

[102]  Peter B Adler,et al.  The effects of intransitive competition on coexistence. , 2017, Ecology letters.

[103]  Costas D. Maranas,et al.  SteadyCom: Predicting microbial abundances while ensuring community stability , 2017, PLoS Comput. Biol..

[104]  Johannes Zimmermann,et al.  BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities , 2017, PLoS Comput. Biol..

[105]  Frank J. Bruggeman,et al.  Model-based quantification of metabolic interactions from dynamic microbial-community data , 2017, PloS one.

[106]  Edward J. O'Brien,et al.  Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow. , 2016, Cell systems.

[107]  Naresh Singhal,et al.  Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems , 2016, Front. Microbiol..

[108]  P. Silver,et al.  Better together: engineering and application of microbial symbioses. , 2015, Current opinion in biotechnology.

[109]  Peter D. Karp,et al.  The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases , 2015, Nucleic Acids Res..

[110]  Philip Miller,et al.  BiGG Models: A platform for integrating, standardizing and sharing genome-scale models , 2015, Nucleic Acids Res..

[111]  Peer Bork,et al.  Metabolic dependencies drive species co-occurrence in diverse microbial communities , 2015, Proceedings of the National Academy of Sciences.

[112]  Torsten Seemann,et al.  Prokka: rapid prokaryotic genome annotation , 2014, Bioinform..

[113]  Rick L. Stevens,et al.  High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource , 2014, Proceedings of the National Academy of Sciences.

[114]  Ali R. Zomorrodi,et al.  d-OptCom: Dynamic multi-level and multi-objective metabolic modeling of microbial communities. , 2014, ACS synthetic biology.

[115]  Juho Rousu,et al.  Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species , 2014, PLoS Comput. Biol..

[116]  Fangfang Xia,et al.  The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST) , 2013, Nucleic Acids Res..

[117]  Joshua A. Lerman,et al.  COBRApy: COnstraints-Based Reconstruction and Analysis for Python , 2013, BMC Systems Biology.

[118]  Costas D. Maranas,et al.  OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities , 2012, PLoS Comput. Biol..

[119]  Joost Boele,et al.  FAME, the Flux Analysis and Modeling Environment , 2012, BMC Systems Biology.

[120]  Radhakrishnan Mahadevan,et al.  Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments , 2011, The ISME Journal.

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

[122]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[123]  OUP accepted manuscript , 2022, Bioinformatics.

[124]  Paul J. Kennedy,et al.  Machine learning and applications in microbiology , 2021, FEMS microbiology reviews.

[125]  G. Zeng,et al.  Sorption, transport and biodegradation - An insight into bioavailability of persistent organic pollutants in soil. , 2018, The Science of the total environment.

[126]  B. Palsson,et al.  A protocol for generating a high-quality genome-scale metabolic reconstruction , 2010, Nature Protocols.

[127]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[128]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..