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Identifying Functional Metabolic Gui...

Reynolds, Ryan C.,

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Identifying Functional Metabolic Guilds: A Computational Approach to Classifying Heterotrophic Diversity in the Marine System /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Identifying Functional Metabolic Guilds: A Computational Approach to Classifying Heterotrophic Diversity in the Marine System // Ryan C Reynolds.
作者:	Reynolds, Ryan C.,
面頁冊數:	1 electronic resource (233 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
標題:	Biological oceanography. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31562828
ISBN:	9798384456063

Identifying Functional Metabolic Guilds: A Computational Approach to Classifying Heterotrophic Diversity in the Marine System /
Reynolds, Ryan C.,

Identifying Functional Metabolic Guilds: A Computational Approach to Classifying Heterotrophic Diversity in the Marine System /Ryan C Reynolds. - 1 electronic resource (233 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Ocean microbial communities are made up of thousands of diverse taxa whose metabolic demands set the rates of both biomass production and degradation. Thus, these microscopic organisms play a critical role in ecosystem dynamics, global carbon cycling, and climate. Modeling these dynamics requires reducing the complexity of microbial communities and linking microbial activities directly with biogeochemical rates. I developed a Bayesian statistical method for defining functional guilds from annotated genomes, derived from both uncultured and cultured organisms. Expanding on this work, I leveraged global metagenomic datasets, metabolic models, and unsupervised machine learning techniques to identify key marine heterotrophs metabolic guilds. I found eight clusters with distinct substrate preferences, growth strategies, taxonomic profiles, and biogeographic distributions. I demonstrated that the slowest growing groups are sensitive to the availability of multiple classes of substrates while the intermediate growth groups are only sensitive to a single class. Moreover, organisms from diverse taxonomic groups can occupy the same metabolic niche such that metabolic strategy cannot always be inferred from taxonomy alone. I also show that the automated metabolic model generation software is both a powerful tool for understanding microbial metabolism and must be used with caution to ensure robust solutions. Overall, this work provides the building blocks for analyzing and modeling diverse marine microbial populations.

English

ISBN: 9798384456063Subjects--Topical Terms:

2122748
Biological oceanography.
Subjects--Index Terms:

Carbon cycling

Identifying Functional Metabolic Guilds: A Computational Approach to Classifying Heterotrophic Diversity in the Marine System /
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520 $a Ocean microbial communities are made up of thousands of diverse taxa whose metabolic demands set the rates of both biomass production and degradation. Thus, these microscopic organisms play a critical role in ecosystem dynamics, global carbon cycling, and climate. Modeling these dynamics requires reducing the complexity of microbial communities and linking microbial activities directly with biogeochemical rates. I developed a Bayesian statistical method for defining functional guilds from annotated genomes, derived from both uncultured and cultured organisms. Expanding on this work, I leveraged global metagenomic datasets, metabolic models, and unsupervised machine learning techniques to identify key marine heterotrophs metabolic guilds. I found eight clusters with distinct substrate preferences, growth strategies, taxonomic profiles, and biogeographic distributions. I demonstrated that the slowest growing groups are sensitive to the availability of multiple classes of substrates while the intermediate growth groups are only sensitive to a single class. Moreover, organisms from diverse taxonomic groups can occupy the same metabolic niche such that metabolic strategy cannot always be inferred from taxonomy alone. I also show that the automated metabolic model generation software is both a powerful tool for understanding microbial metabolism and must be used with caution to ensure robust solutions. Overall, this work provides the building blocks for analyzing and modeling diverse marine microbial populations.
546 $a English
590 $a School code: 0208
650 4 $a Biological oceanography. $3 2122748
650 4 $a Bioinformatics. $3 553671
650 4 $a Microbiology. $3 536250
650 4 $a Engineering. $3 586835
650 4 $a Biogeochemistry. $3 545717
653 $a Carbon cycling
653 $a Functional guilds
653 $a Heterotrophic metabolism
653 $a Marine heterotrophs
653 $a Microbial communities
653 $a Microbial metabolism
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690 $a 0715
690 $a 0410
690 $a 0537
690 $a 0425
710 2 $a University of Southern California. $b Biology. $e degree granting institution. $3 3816950
720 1 $a Levine, Naomi M. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-03B.
790 $a 0208
791 $a Ph.D.
792 $a 2024
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