@article{
 title = {Challenges in modeling the human gut microbiome},
 type = {article},
 id = {a3b665c1-6181-32fa-8fc7-33e2eec7572e},
 created = {2018-06-12T19:15:16.128Z},
 file_attached = {true},
 profile_id = {4dd55f2f-c613-3fe1-a600-5819fb65fcff},
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 last_modified = {2018-06-12T19:15:16.388Z},
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 abstract = {A paper by Magnusdottir et al. 1 in a previous issue describes the generation of genome-scale 5 metabolic models (GEMs) for 773 members of the human gut microbiota, referred to as 6 AGORA. It represents a valuable contribution to quantitative analyses of how the many 7 different species of gut microbiota interact and impact host metabolism. At the same time, a 8 detailed analysis of the models described in this work raises several questions. 9 Evidence is growing that differences in gut microbiota may have an impact on human 10 health, including cardiovascular disease 2 and type 2 diabetes 3–5 , but so far no studies have 11 demonstrated causal relationships. Mathematical modeling of the gut microbiota makes it 12 possible to evaluate different hypothesis and thereby gain mechanistic insight into how the 13 gut microbiota composition affects host metabolism. GEM analysis is particularly well suited 14 for this purpose as it is possible to reconstruct the metabolic networks of gut symbionts based 15 on genomic information and then use constraint-based modeling, often referred to as flux 16 balance analysis (FBA), for simulation of their metabolic functions 6 . This modeling concept 17 has been validated for its ability to correctly simulate the metabolism of colonized bacteria in 18 germ-free mice 7 and for being able to predict how the levels of key metabolites in fecal water 19 or plasma are influenced by gut microbiota composition 8 . Furthermore, using this concept, 20 computational tools and frameworks have been developed to allocate metabolic resources to 21 individual microbes and infer the ecological interaction, spatial dynamics and community-22 level assembly processes 9,10 . 23 The paper by Magnusdottir et al. describes AGORA, a resource of GEMs for >700 gut 24 microbes that accounts for most of the dominant species in the human gut microbiota. Here 25 we point out several drawbacks associated with these GEMs as functional models for 26 simulating metabolic interactions between either gut symbionts or the gut microbiota and their 27 host. 28 A quality check of the 773 GEMs in AGORA suggests that most of the models are 29 incapable of quantitative predictions without further manual curation and support of 30 experimental data. We illustrate this by benchmarking all 773 AGORA GEMs against 70 31 metabolic models from BiGG 11 , another GEM repository. 32 First, we evaluated the ability of the AGORA models to predict growth. The COBRA 33 toolbox was used to calculate the specific growth rate of the AGORA GEMs using the glpk 34},
 bibtype = {article},
 author = {}
}

{"_id":"cPZMmgo27gjEkcyj8","bibbaseid":"challengesinmodelingthehumangutmicrobiome","downloads":0,"creationDate":"2018-10-07T18:34:29.853Z","title":"Challenges in modeling the human gut microbiome","author_short":[],"year":null,"bibtype":"article","biburl":null,"bibdata":{"title":"Challenges in modeling the human gut microbiome","type":"article","id":"a3b665c1-6181-32fa-8fc7-33e2eec7572e","created":"2018-06-12T19:15:16.128Z","file_attached":"true","profile_id":"4dd55f2f-c613-3fe1-a600-5819fb65fcff","group_id":"e0d9c14f-8342-389b-84fc-a19963b606ff","last_modified":"2018-06-12T19:15:16.388Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"folder_uuids":"9ecde1f2-fbc7-413d-b8a2-7f450155961e","private_publication":false,"abstract":"A paper by Magnusdottir et al. 1 in a previous issue describes the generation of genome-scale 5 metabolic models (GEMs) for 773 members of the human gut microbiota, referred to as 6 AGORA. It represents a valuable contribution to quantitative analyses of how the many 7 different species of gut microbiota interact and impact host metabolism. At the same time, a 8 detailed analysis of the models described in this work raises several questions. 9 Evidence is growing that differences in gut microbiota may have an impact on human 10 health, including cardiovascular disease 2 and type 2 diabetes 3–5 , but so far no studies have 11 demonstrated causal relationships. Mathematical modeling of the gut microbiota makes it 12 possible to evaluate different hypothesis and thereby gain mechanistic insight into how the 13 gut microbiota composition affects host metabolism. GEM analysis is particularly well suited 14 for this purpose as it is possible to reconstruct the metabolic networks of gut symbionts based 15 on genomic information and then use constraint-based modeling, often referred to as flux 16 balance analysis (FBA), for simulation of their metabolic functions 6 . This modeling concept 17 has been validated for its ability to correctly simulate the metabolism of colonized bacteria in 18 germ-free mice 7 and for being able to predict how the levels of key metabolites in fecal water 19 or plasma are influenced by gut microbiota composition 8 . Furthermore, using this concept, 20 computational tools and frameworks have been developed to allocate metabolic resources to 21 individual microbes and infer the ecological interaction, spatial dynamics and community-22 level assembly processes 9,10 . 23 The paper by Magnusdottir et al. describes AGORA, a resource of GEMs for >700 gut 24 microbes that accounts for most of the dominant species in the human gut microbiota. Here 25 we point out several drawbacks associated with these GEMs as functional models for 26 simulating metabolic interactions between either gut symbionts or the gut microbiota and their 27 host. 28 A quality check of the 773 GEMs in AGORA suggests that most of the models are 29 incapable of quantitative predictions without further manual curation and support of 30 experimental data. We illustrate this by benchmarking all 773 AGORA GEMs against 70 31 metabolic models from BiGG 11 , another GEM repository. 32 First, we evaluated the ability of the AGORA models to predict growth. The COBRA 33 toolbox was used to calculate the specific growth rate of the AGORA GEMs using the glpk 34","bibtype":"article","author":[],"bibtex":"@article{\n title = {Challenges in modeling the human gut microbiome},\n type = {article},\n id = {a3b665c1-6181-32fa-8fc7-33e2eec7572e},\n created = {2018-06-12T19:15:16.128Z},\n file_attached = {true},\n profile_id = {4dd55f2f-c613-3fe1-a600-5819fb65fcff},\n group_id = {e0d9c14f-8342-389b-84fc-a19963b606ff},\n last_modified = {2018-06-12T19:15:16.388Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {9ecde1f2-fbc7-413d-b8a2-7f450155961e},\n private_publication = {false},\n abstract = {A paper by Magnusdottir et al. 1 in a previous issue describes the generation of genome-scale 5 metabolic models (GEMs) for 773 members of the human gut microbiota, referred to as 6 AGORA. It represents a valuable contribution to quantitative analyses of how the many 7 different species of gut microbiota interact and impact host metabolism. At the same time, a 8 detailed analysis of the models described in this work raises several questions. 9 Evidence is growing that differences in gut microbiota may have an impact on human 10 health, including cardiovascular disease 2 and type 2 diabetes 3–5 , but so far no studies have 11 demonstrated causal relationships. Mathematical modeling of the gut microbiota makes it 12 possible to evaluate different hypothesis and thereby gain mechanistic insight into how the 13 gut microbiota composition affects host metabolism. GEM analysis is particularly well suited 14 for this purpose as it is possible to reconstruct the metabolic networks of gut symbionts based 15 on genomic information and then use constraint-based modeling, often referred to as flux 16 balance analysis (FBA), for simulation of their metabolic functions 6 . This modeling concept 17 has been validated for its ability to correctly simulate the metabolism of colonized bacteria in 18 germ-free mice 7 and for being able to predict how the levels of key metabolites in fecal water 19 or plasma are influenced by gut microbiota composition 8 . Furthermore, using this concept, 20 computational tools and frameworks have been developed to allocate metabolic resources to 21 individual microbes and infer the ecological interaction, spatial dynamics and community-22 level assembly processes 9,10 . 23 The paper by Magnusdottir et al. describes AGORA, a resource of GEMs for >700 gut 24 microbes that accounts for most of the dominant species in the human gut microbiota. Here 25 we point out several drawbacks associated with these GEMs as functional models for 26 simulating metabolic interactions between either gut symbionts or the gut microbiota and their 27 host. 28 A quality check of the 773 GEMs in AGORA suggests that most of the models are 29 incapable of quantitative predictions without further manual curation and support of 30 experimental data. We illustrate this by benchmarking all 773 AGORA GEMs against 70 31 metabolic models from BiGG 11 , another GEM repository. 32 First, we evaluated the ability of the AGORA models to predict growth. 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