Centner, T. J. & Heric, D. C. Anti-community state pesticide preemption laws prevent local governments from protecting people from harm. Int. J. Agric. Sustain. 17, 118–126. https://doi.org/10.1080/14735903.2019.1568814 (2019).
Lopez-Pacheco, I. Y. et al. Anthropogenic contaminants of high concern: Existence in water resources and their adverse effects. Sci. Total Environ. 690, 1068–1088. https://doi.org/10.1016/j.scitotenv.2019.07.052 (2019).
Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358. https://doi.org/10.1371/journal.pcbi.1002358 (2012).
Rani, M., Shanker, U. & Jassal, V. Recent strategies for removal and degradation of persistent & toxic organochlorine pesticides using nanoparticles: A review. J. Environ. Manage. 190, 208–222. https://doi.org/10.1016/j.jenvman.2016.12.068 (2017).
Arcuri, A. & Hendlin, Y. H. The chemical anthropocene: Glyphosate as a case study of pesticide exposures. King’s Law J. 30, 234–253. https://doi.org/10.1080/09615768.2019.1645436 (2019).
Agency, U. S. E. P. Atrazine—Background and Updates. https://www.epa.gov/ingredients-used-pesticide-products/atrazine-background-and-updates.
Kastner, M. & Miltner, A. Application of compost for effective bioremediation of organic contaminants and pollutants in soil. Appl. Microbiol. Biotechnol. 100, 3433–3449. https://doi.org/10.1007/s00253-016-7378-y (2016).
Kanissery, R. G. & Sims, G. K. Biostimulation for the enhanced degradation of herbicides in soil. Appl. Environ. Soil Sci. 2011, 10. https://doi.org/10.1155/2011/843450 (2011).
Viegas, C. A. et al. Evaluating formulation and storage of Arthrobacter aurescens strain TC1 as a bioremediation tool for terbuthylazine contaminated soils: Efficacy on abatement of aquatic ecotoxicity. Sci. Total Environ. 668, 714–722. https://doi.org/10.1016/j.scitotenv.2019.02.355 (2019).
Perez-Garcia, O., Lear, G. & Singhal, N. Metabolic network modeling of microbial interactions in natural and engineered environmental systems. Front. Microbiol. 7, 673. https://doi.org/10.3389/fmicb.2016.00673 (2016).
Nilsson, T., Rova, M. & Backlund, A. S. Microbial metabolism of oxochlorates: A bioenergetic perspective. Biochim. Biophys. Acta 1827, 189–197. https://doi.org/10.1016/j.bbabio.2012.06.010 (2013).
Mani, D. & Kumar, C. Biotechnological advances in bioremediation of heavy metals contaminated ecosystems: An overview with special reference to phytoremediation. Int. J. Environ. Sci. Technol. 11, 843–872. https://doi.org/10.1007/s13762-013-0299-8 (2013).
Shah, J. & Dahanukar, N. Bioremediation of organometallic compounds by bacterial degradation. Indian J. Microbiol. 52, 300–304. https://doi.org/10.1007/s12088-011-0223-1 (2012).
Megharaj, M., Ramakrishnan, B., Venkateswarlu, K., Sethunathan, N. & Naidu, R. Bioremediation approaches for organic pollutants: A critical perspective. Environ. Int. 37, 1362–1375. https://doi.org/10.1016/j.envint.2011.06.003 (2011).
Nousiainen, A. O. et al. Bioremediation strategies for removal of residual atrazine in the boreal groundwater zone. Appl. Microbiol. Biotechnol. 99, 10249–10259. https://doi.org/10.1007/s00253-015-6828-2 (2015).
Sagarkar, S. et al. Soil mesocosm studies on atrazine bioremediation. J. Environ. Manag. 139, 208–216. https://doi.org/10.1016/j.jenvman.2014.02.016 (2014).
Fan, X. & Song, F. Bioremediation of atrazine: Recent advances and promises. J. Soils Sediments 14, 1727–1737. https://doi.org/10.1007/s11368-014-0921-5 (2014).
Singh, B. & Singh, K. Microbial degradation of herbicides. Crit. Rev. Microbiol. 42, 245–261. https://doi.org/10.3109/1040841X.2014.929564 (2016).
Adams, G. O., Fufeyin, P. T., Okoro, S. E. & Ehinomen, I. Bioremediation, biostimulation and bioaugmention: A review. Int. J. Environ. Bioremed. Biodegrad. 3, 28–39 (2015).
Zhao, X., Bai, S., Li, C., Yang, J. & Ma, F. Bioaugmentation of atrazine removal in constructed wetland: Performance, microbial dynamics, and environmental impacts. Biores. Technol. 289, 121618. https://doi.org/10.1016/j.biortech.2019.121618 (2019).
Wang, Q. & Xie, S. Isolation and characterization of a high-efficiency soil atrazine-degrading Arthrobacter sp. strain. Int. Biodeteriorat. Biodegrad. 71, 61–66. https://doi.org/10.1016/j.ibiod.2012.04.005 (2012).
Fang, H., Lian, J., Wang, H., Cai, L. & Yu, Y. Exploring bacterial community structure and function associated with atrazine biodegradation in repeatedly treated soils. J. Hazard. Mater. 286, 457–465. https://doi.org/10.1016/j.jhazmat.2015.01.006 (2015).
Zhang, Y. et al. Metabolic ability and individual characteristics of an atrazine-degrading consortium DNC5. J. Hazard Mater. 237–238, 376–381. https://doi.org/10.1016/j.jhazmat.2012.08.047 (2012).
Strong, L. C., Rosendahl, C., Johnson, G., Sadowsky, M. J. & Wackett, L. P. Arthrobacter aurescens TC1 metabolizes diverse s-triazine ring compounds. Appl. Environ. Microbiol. 68, 5973–5980 (2002).
Sagarkar, S. et al. Monitoring bioremediation of atrazine in soil microcosms using molecular tools. Environ. Pollut. 172, 108–115. https://doi.org/10.1016/j.envpol.2012.07.048 (2013).
Silva, V. P. et al. Evaluation of Arthrobacter aurescens strain TC1 as bioaugmentation bacterium in soils contaminated with the herbicidal substance terbuthylazine. PLoS ONE 10, e0144978 (2015).
Deutch, C. E., Bui, A. P. & Ho, T. Growth of Paenarthrobacter aurescens strain TC1 on atrazine and isopropylamine during osmotic stress. Ann. Microbiol. 68, 569–577. https://doi.org/10.1007/s13213-018-1364-9 (2018).
Xu, C. et al. Genome-scale metabolic model in guiding metabolic engineering of microbial improvement. Appl. Microbiol. Biotechnol. 97, 519–539. https://doi.org/10.1007/s00253-012-4543-9 (2013).
Contador, C. A., Rodriguez, V., Andrews, B. A. & Asenjo, J. A. Genome-scale reconstruction of Salinispora tropica CNB-440 metabolism to study strain-specific adaptation. Antonie Van Leeuwenhoek 108, 1075–1090. https://doi.org/10.1007/s10482-015-0561-9 (2015).
Mahadevan, R. & Lovley, D. R. The degree of redundancy in metabolic genes is linked to mode of metabolism. Biophys. J. 94, 1216–1220. https://doi.org/10.1529/biophysj.107.118414 (2008).
Mahadevan, R. et al. Characterization of metabolism in the Fe(III)-reducing organism Geobacter sulfurreducens by constraint-based modeling. Appl. Environ. Microbiol. 72, 1558–1568. https://doi.org/10.1128/AEM.72.2.1558-1568.2006 (2006).
Oberhardt, M. A. et al. Harnessing the landscape of microbial culture media to predict new organism-media pairings. Nat. Commun. 6, 8493. https://doi.org/10.1038/ncomms9493 (2015).
Lee, J., Yun, H., Feist, A. M., Palsson, B. Ø & Lee, S. Y. Genome-scale reconstruction and in silico analysis of the Clostridium acetobutylicum ATCC 824 metabolic network. Appl. Microbiol. Biotechnol. https://doi.org/10.1007/s00253-008-1654-4 (2008).
Thiele, I., Vo, T. D., Price, N. D. & Palsson, B. O. Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): An in silico genome-scale characterization of single- and double-deletion mutants. J. Bacteriol. 187, 5818–5830. https://doi.org/10.1128/JB.187.16.5818-5830.2005 (2005).
Price, N. D., Papin, J. A., Schilling, C. H. & Palsson, B. O. Genome-scale microbial in silico models: The constraints-based approach. Trends Biotechnol. 21, 162–169. https://doi.org/10.1016/S0167-7799(03)00030-1 (2003).
Reed, J. L., Vo, T. D., Schilling, C. H. & Palsson, B. O. An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol. 4, R54. https://doi.org/10.1186/gb-2003-4-9-r54 (2003).
Fang, Y. et al. Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model. J. Contam. Hydrol. 122, 96–103. https://doi.org/10.1016/j.jconhyd.2010.11.007 (2011).
Zomorrodi, A. R. & Segre, D. Synthetic ecology of microbes: Mathematical models and applications. J. Mol. Biol. 428, 837–861. https://doi.org/10.1016/j.jmb.2015.10.019 (2016).
Biswas, K., Paul, D. & Sinha, S. N. Biological agents of bioremediation: A concise review. Microbiology 1, 39–43 (2015).
Henson, M. A. Genome-scale modelling of microbial metabolism with temporal and spatial resolution. Biochem. Soc. Trans. 43, 1164–1171. https://doi.org/10.1042/BST20150146 (2015).
Cordova, L. T., Long, C. P., Venkataramanan, K. P. & Antoniewicz, M. R. Complete genome sequence, metabolic model construction and phenotypic characterization of Geobacillus LC300, an extremely thermophilic, fast growing, xylose-utilizing bacterium. Metab. Eng. 32, 74–81. https://doi.org/10.1016/j.ymben.2015.09.009 (2015).
de la Torre, A. et al. Genome-scale metabolic reconstructions and theoretical investigation of methane conversion in Methylomicrobium buryatense strain 5G(B1). Microb. Cell Fact. 14, 188. https://doi.org/10.1186/s12934-015-0377-3 (2015).
Zou, W. et al. Reconstruction and analysis of a genome-scale metabolic model of the vitamin C producing industrial strain Ketogulonicigenium vulgare WSH-001. J. Biotechnol. 161, 42–48. https://doi.org/10.1016/j.jbiotec.2012.05.015 (2012).
Pacheco, M. P., Bintener, T. & Sauter, T. Towards the network-based prediction of repurposed drugs using patient-specific metabolic models. EBioMedicine https://doi.org/10.1016/j.ebiom.2019.04.017 (2019).
Izallalen, M. et al. Geobacter sulfurreducens strain engineered for increased rates of respiration. Metab. Eng. 10, 267–275. https://doi.org/10.1016/j.ymben.2008.06.005 (2008).
Burgard, A. P., Pharkya, P. & Maranas, C. D. Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84, 647–657. https://doi.org/10.1002/bit.10803 (2003).
Ang, K. S., Lakshmanan, M., Lee, N. R. & Lee, D. Y. Metabolic modeling of microbial community interactions for health, environmental and biotechnological applications. Curr. Genom. 19, 712–722. https://doi.org/10.2174/1389202919666180911144055 (2018).
Zhuang, K. et al. Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J. 5, 305–316. https://doi.org/10.1038/ismej.2010.117 (2011).
Xu, X. et al. Modeling microbial communities from atrazine contaminated soils promotes the development of biostimulation solutions. ISME J. 13, 494–508. https://doi.org/10.1038/s41396-018-0288-5 (2019).
Faust, K. Microbial consortium design benefits from metabolic modeling. Trends Biotechnol. 37, 123–125. https://doi.org/10.1016/j.tibtech.2018.11.004 (2019).
Mongodin, E. F. et al. Secrets of soil survival revealed by the genome sequence of Arthrobacter aurescens TC1. PLoS Genet. 2, e214. https://doi.org/10.1371/journal.pgen.0020214 (2006).
Henry, C. S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28, 977–982. https://doi.org/10.1038/nbt.1672 (2010).
Oh, Y. K., Palsson, B. O., Park, S. M., Schilling, C. H. & Mahadevan, R. Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J. Biol. Chem. 282, 28791–28799. https://doi.org/10.1074/jbc.M703759200 (2007).
Thiele, I. & Palsson, B. O. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5, 93–121. https://doi.org/10.1038/nprot.2009.203 (2010).
Meyer, F. et al. The metagenomics RAST server—A public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform. 9, 1–8. https://doi.org/10.1186/1471-2105-9-386 (2008).
Kanehisa, M. et al. Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res. 42, D199-205. https://doi.org/10.1093/nar/gkt1076 (2014).
Nordberg, H. et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res. 42, D26-31. https://doi.org/10.1093/nar/gkt1069 (2014).
Apweiler, R. et al. UniProt: The Universal Protein knowledgebase. Nucleic Acids Res. 32, D115-119. https://doi.org/10.1093/nar/gkh131 (2004).
Kundu, K. et al. Defining lower limits of biodegradation: Atrazine degradation regulated by mass transfer and maintenance demand in Arthrobacter aurescens TC1. ISME J. https://doi.org/10.1038/s41396-019-0430-z (2019).
Larocque, M., Chenard, T. & Najmanovich, R. A curated C. difficile strain 630 metabolic network: Prediction of essential targets and inhibitors. BMC Syst. Biol. 8, 117. https://doi.org/10.1186/s12918-014-0117-z (2014).
Monk, J. M. et al. iML1515, a knowledgebase that computes Escherichia coli traits. Nat. Biotechnol. 35, 904–908. https://doi.org/10.1038/nbt.3956 (2017).
Hucka, M. et al. The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531. https://doi.org/10.1093/bioinformatics/btg015 (2003).
Govantes, F. et al. Regulation of the atrazine-degradative genes in Pseudomonas sp. strain ADP. FEMS Microbiol. Lett. 310, 1–8. https://doi.org/10.1111/j.1574-6968.2010.01991.x (2010).
Guo, Q., Zhang, J., Wan, R. & Xie, S. Impacts of carbon sources on simazine biodegradation by Arthrobacter strain SD3-25 in liquid culture and soil microcosm. Int. Biodeterior. Biodegrad. 89, 1–6 (2014).
Dsouza, M., Taylor, M. W., Turner, S. J. & Aislabie, J. Genomic and phenotypic insights into the ecology of Arthrobacter from Antarctic soils. BMC Genom. 16, 36. https://doi.org/10.1186/s12864-015-1220-2 (2015).
Deutch, C. E. L-Proline catabolism by the high G+C Gram-positive bacterium Paenarthrobacter aurescens strain TC1. Antonie Van Leeuwenhoek 112, 237–251. https://doi.org/10.1007/s10482-018-1148-z (2019).
Mahadevan, R. & Schilling, C. H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264–276 (2003).
Zhang, Q. et al. Impacts of nitrogen and phosphorus on atrazine-contaminated soil remediation and detoxification by Arthrobacter sp. strain HB-5. Environ. Earth Sci. 71, 1465–1471 (2014).
Lachance, J. C. et al. BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. PLoS Comput. Biol. 15, e1006971. https://doi.org/10.1371/journal.pcbi.1006971 (2019).
Reed, J. L., Famili, I., Thiele, I. & Palsson, B. O. Towards multidimensional genome annotation. Nat. Rev. Genet. 7, 130–141. https://doi.org/10.1038/nrg1769 (2006).
Casida, J. E. & Bryant, R. J. The ABCs of pesticide toxicology: Amounts, biology, and chemistry. Toxicol. Res. (Camb.) 6, 755–763. https://doi.org/10.1039/c7tx00198c (2017).
King, Z. A. et al. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 44, D515-522. https://doi.org/10.1093/nar/gkv1049 (2016).
Varma, A. & Palsson, B. O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 60, 3724–3731 (1994).
Orth, J. D., Thiele, I. & Palsson, B. O. What is flux balance analysis?. Nat. Biotechnol. 28, 245–248. https://doi.org/10.1038/nbt.1614 (2010).
Harcombe, W. R. et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep. 7, 1104–1115. https://doi.org/10.1016/j.celrep.2014.03.070 (2014).
King, Z. A. et al. Escher: A web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS Comput. Biol. 11, e1004321. https://doi.org/10.1371/journal.pcbi.1004321 (2015).
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