Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup
Activated sludge (AS) microcosm experiments usually begin with inoculating a bioreactor with an AS mixed culture. During the bioreactor startup, AS communities undergo, to some extent, a distortion in their characteristics (e.g., loss of diversity). This work aimed to provide a predictive understand...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Microorganisms |
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| Online Access: | https://www.mdpi.com/2076-2607/9/7/1387 |
| _version_ | 1797528693492416512 |
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| author | Youngjun Kim Sangeun Park Seungdae Oh |
| author_facet | Youngjun Kim Sangeun Park Seungdae Oh |
| author_sort | Youngjun Kim |
| collection | DOAJ |
| description | Activated sludge (AS) microcosm experiments usually begin with inoculating a bioreactor with an AS mixed culture. During the bioreactor startup, AS communities undergo, to some extent, a distortion in their characteristics (e.g., loss of diversity). This work aimed to provide a predictive understanding of the dynamic changes in the community structure and diversity occurring during aerobic AS microcosm startups. AS microcosms were developed using three frequently used carbon sources: acetate (A), glucose (G), and starch (S), respectively. A mathematical modeling approach quantitatively determined that 1.7–2.4 times the solid retention time (SRT) was minimally required for the microcosm startups, during which substantial divergences in the community biomass and diversity (33–45% reduction in species richness and diversity) were observed. A machine learning modeling application using AS microbiome data could successfully (>95% accuracy) predict the assembly pattern of aerobic AS microcosm communities responsive to each carbon source. A feature importance analysis pinpointed specific taxa that were highly indicative of a microcosm feed source (A, G, or S) and significantly contributed for the ML-based predictive classification. The results of this study have important implications on the interpretation and validity of microcosm experiments using AS. |
| first_indexed | 2024-03-10T10:02:57Z |
| format | Article |
| id | doaj.art-c0b9906a71f8448c9779a7ef3ae37b5f |
| institution | Directory Open Access Journal |
| issn | 2076-2607 |
| language | English |
| last_indexed | 2024-03-10T10:02:57Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Microorganisms |
| spelling | doaj.art-c0b9906a71f8448c9779a7ef3ae37b5f2023-11-22T01:49:39ZengMDPI AGMicroorganisms2076-26072021-06-0197138710.3390/microorganisms9071387Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm StartupYoungjun Kim0Sangeun Park1Seungdae Oh2Department of Civil Engineering, Kyung Hee University, Yongin-si 17104, KoreaDepartment of Civil Engineering, Kyung Hee University, Yongin-si 17104, KoreaDepartment of Civil Engineering, Kyung Hee University, Yongin-si 17104, KoreaActivated sludge (AS) microcosm experiments usually begin with inoculating a bioreactor with an AS mixed culture. During the bioreactor startup, AS communities undergo, to some extent, a distortion in their characteristics (e.g., loss of diversity). This work aimed to provide a predictive understanding of the dynamic changes in the community structure and diversity occurring during aerobic AS microcosm startups. AS microcosms were developed using three frequently used carbon sources: acetate (A), glucose (G), and starch (S), respectively. A mathematical modeling approach quantitatively determined that 1.7–2.4 times the solid retention time (SRT) was minimally required for the microcosm startups, during which substantial divergences in the community biomass and diversity (33–45% reduction in species richness and diversity) were observed. A machine learning modeling application using AS microbiome data could successfully (>95% accuracy) predict the assembly pattern of aerobic AS microcosm communities responsive to each carbon source. A feature importance analysis pinpointed specific taxa that were highly indicative of a microcosm feed source (A, G, or S) and significantly contributed for the ML-based predictive classification. The results of this study have important implications on the interpretation and validity of microcosm experiments using AS.https://www.mdpi.com/2076-2607/9/7/1387machine learningmicrocosmactivated sludgereactor startupcarbon source |
| spellingShingle | Youngjun Kim Sangeun Park Seungdae Oh Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup Microorganisms machine learning microcosm activated sludge reactor startup carbon source |
| title | Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup |
| title_full | Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup |
| title_fullStr | Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup |
| title_full_unstemmed | Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup |
| title_short | Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup |
| title_sort | machine learning approach reveals the assembly of activated sludge microbiome with different carbon sources during microcosm startup |
| topic | machine learning microcosm activated sludge reactor startup carbon source |
| url | https://www.mdpi.com/2076-2607/9/7/1387 |
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