@article{
 title = {FLYCOP: metabolic modeling-based analysis and engineering microbial communities},
 type = {article},
 year = {2018},
 pages = {i954-i963},
 volume = {34},
 websites = {https://academic.oup.com/bioinformatics/article/34/17/i954/5093244},
 month = {9},
 publisher = {Narnia},
 day = {1},
 id = {155e7a70-c635-30d4-965e-b8170fa13423},
 created = {2019-09-30T15:36:16.366Z},
 accessed = {2019-09-30},
 file_attached = {false},
 profile_id = {fa910c8b-8889-3a42-afc9-302da7e3933a},
 group_id = {ff1f9038-dd83-321a-9605-910d757253bb},
 last_modified = {2019-09-30T15:53:51.231Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Motivation
Synthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account.

Results
In contrast to previous systems optimizing microbial consortia, FLYCOP has novel characteristics to face up to new problems, to represent additional features and to analyze events influencing the consortia behavior. In this manuscript, FLYCOP optimizes a Synechococcus elongatus-Pseudomonas putida consortium to produce the maximum amount of bio-plastic (PHA, polyhydroxyalkanoate), and highlights the influence of metabolites exchange dynamics in a four auxotrophic Escherichia coli consortium with parallel growth. FLYCOP can also provide an explanation about biological evolution driving evolutionary engineering endeavors by describing why and how heterogeneous populations emerge from monoclonal ones.},
 bibtype = {article},
 author = {García-Jiménez, Beatriz and García, José Luis and Nogales, Juan},
 doi = {10.1093/bioinformatics/bty561},
 journal = {Bioinformatics},
 number = {17}
}

{"_id":"q3jDxBZLqc8FuAjKi","bibbaseid":"garcajimnez-garca-nogales-flycopmetabolicmodelingbasedanalysisandengineeringmicrobialcommunities-2018","authorIDs":["29mqFgC8YggrGCBbZ","2ffhnL4mxZJ9EvWgW","5PeAp7WLPtHfvc7Gt","5d921c8b149a41db010002bb","5dfa0fd72e791dde010000b6","5e138dfea212e1de01000101","5e21c50596aea7de0100019f","5e26bf5b8535cedf01000058","5e26d68742065ede01000035","5e27f2f04d75d8de01000048","5e347e97fae8b9de010000eb","5e39b195ad94f9de010001ac","5e3c320934cd37de0100012b","5e48413800973ede01000046","5e5651006f0b61df01000061","5e58423d1f3fc8de01000184","PvGyEGkeMSiJMLJ8F","SkB5uHXa6iZzhoSdw","TD2qrHqr5dqaSagkZ","b9j5u9qNYgmPzx3rD","fm7m9NXNBgs7hYHnA","pfTzJ3PYtfhhTbQPB","rbYFkg24XeYFhqc74","umi7QMjQNJZgpvcJv","vYxi2AvEfbXv3L3yx","zTw6W3kTiuRT7TGS9"],"author_short":["García-Jiménez, B.","García, J., L.","Nogales, J."],"bibdata":{"title":"FLYCOP: metabolic modeling-based analysis and engineering microbial communities","type":"article","year":"2018","pages":"i954-i963","volume":"34","websites":"https://academic.oup.com/bioinformatics/article/34/17/i954/5093244","month":"9","publisher":"Narnia","day":"1","id":"155e7a70-c635-30d4-965e-b8170fa13423","created":"2019-09-30T15:36:16.366Z","accessed":"2019-09-30","file_attached":false,"profile_id":"fa910c8b-8889-3a42-afc9-302da7e3933a","group_id":"ff1f9038-dd83-321a-9605-910d757253bb","last_modified":"2019-09-30T15:53:51.231Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Motivation\r\nSynthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account.\r\n\r\nResults\r\nIn contrast to previous systems optimizing microbial consortia, FLYCOP has novel characteristics to face up to new problems, to represent additional features and to analyze events influencing the consortia behavior. In this manuscript, FLYCOP optimizes a Synechococcus elongatus-Pseudomonas putida consortium to produce the maximum amount of bio-plastic (PHA, polyhydroxyalkanoate), and highlights the influence of metabolites exchange dynamics in a four auxotrophic Escherichia coli consortium with parallel growth. FLYCOP can also provide an explanation about biological evolution driving evolutionary engineering endeavors by describing why and how heterogeneous populations emerge from monoclonal ones.","bibtype":"article","author":"García-Jiménez, Beatriz and García, José Luis and Nogales, Juan","doi":"10.1093/bioinformatics/bty561","journal":"Bioinformatics","number":"17","bibtex":"@article{\n title = {FLYCOP: metabolic modeling-based analysis and engineering microbial communities},\n type = {article},\n year = {2018},\n pages = {i954-i963},\n volume = {34},\n websites = {https://academic.oup.com/bioinformatics/article/34/17/i954/5093244},\n month = {9},\n publisher = {Narnia},\n day = {1},\n id = {155e7a70-c635-30d4-965e-b8170fa13423},\n created = {2019-09-30T15:36:16.366Z},\n accessed = {2019-09-30},\n file_attached = {false},\n profile_id = {fa910c8b-8889-3a42-afc9-302da7e3933a},\n group_id = {ff1f9038-dd83-321a-9605-910d757253bb},\n last_modified = {2019-09-30T15:53:51.231Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Motivation\r\nSynthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account.\r\n\r\nResults\r\nIn contrast to previous systems optimizing microbial consortia, FLYCOP has novel characteristics to face up to new problems, to represent additional features and to analyze events influencing the consortia behavior. In this manuscript, FLYCOP optimizes a Synechococcus elongatus-Pseudomonas putida consortium to produce the maximum amount of bio-plastic (PHA, polyhydroxyalkanoate), and highlights the influence of metabolites exchange dynamics in a four auxotrophic Escherichia coli consortium with parallel growth. FLYCOP can also provide an explanation about biological evolution driving evolutionary engineering endeavors by describing why and how heterogeneous populations emerge from monoclonal ones.},\n bibtype = {article},\n author = {García-Jiménez, Beatriz and García, José Luis and Nogales, Juan},\n doi = {10.1093/bioinformatics/bty561},\n journal = {Bioinformatics},\n number = {17}\n}","author_short":["García-Jiménez, B.","García, J., L.","Nogales, J."],"urls":{"Website":"https://academic.oup.com/bioinformatics/article/34/17/i954/5093244"},"biburl":"https://bibbase.org/service/mendeley/fa910c8b-8889-3a42-afc9-302da7e3933a","bibbaseid":"garcajimnez-garca-nogales-flycopmetabolicmodelingbasedanalysisandengineeringmicrobialcommunities-2018","role":"author","metadata":{"authorlinks":{"garcía-jiménez, b":"https://bibbase.org/service/mendeley/fa910c8b-8889-3a42-afc9-302da7e3933a/group/ff1f9038-dd83-321a-9605-910d757253bb"}},"downloads":5},"bibtype":"article","creationDate":"2019-09-30T15:49:11.382Z","downloads":5,"keywords":[],"search_terms":["flycop","metabolic","modeling","based","analysis","engineering","microbial","communities","garcía-jiménez","garcía","nogales"],"title":"FLYCOP: metabolic modeling-based analysis and engineering microbial communities","year":2018,"biburl":"https://bibbase.org/service/mendeley/fa910c8b-8889-3a42-afc9-302da7e3933a","dataSources":["tq9Mn6vr6PzLmZdPe","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}