Inferring microbial interactions with their environment from genomic and metagenomic data.
Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has le...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-11-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011661&type=printable |
| _version_ | 1797394674657263616 |
|---|---|
| author | James D Brunner Laverne A Gallegos-Graves Marie E Kroeger |
| author_facet | James D Brunner Laverne A Gallegos-Graves Marie E Kroeger |
| author_sort | James D Brunner |
| collection | DOAJ |
| description | Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks. |
| first_indexed | 2024-03-09T00:23:12Z |
| format | Article |
| id | doaj.art-53143ee0a5764707a0849996302d05e7 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-09T00:23:12Z |
| publishDate | 2023-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-53143ee0a5764707a0849996302d05e72023-12-12T05:31:49ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-11-011911e101166110.1371/journal.pcbi.1011661Inferring microbial interactions with their environment from genomic and metagenomic data.James D BrunnerLaverne A Gallegos-GravesMarie E KroegerMicrobial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011661&type=printable |
| spellingShingle | James D Brunner Laverne A Gallegos-Graves Marie E Kroeger Inferring microbial interactions with their environment from genomic and metagenomic data. PLoS Computational Biology |
| title | Inferring microbial interactions with their environment from genomic and metagenomic data. |
| title_full | Inferring microbial interactions with their environment from genomic and metagenomic data. |
| title_fullStr | Inferring microbial interactions with their environment from genomic and metagenomic data. |
| title_full_unstemmed | Inferring microbial interactions with their environment from genomic and metagenomic data. |
| title_short | Inferring microbial interactions with their environment from genomic and metagenomic data. |
| title_sort | inferring microbial interactions with their environment from genomic and metagenomic data |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011661&type=printable |
| work_keys_str_mv | AT jamesdbrunner inferringmicrobialinteractionswiththeirenvironmentfromgenomicandmetagenomicdata AT laverneagallegosgraves inferringmicrobialinteractionswiththeirenvironmentfromgenomicandmetagenomicdata AT marieekroeger inferringmicrobialinteractionswiththeirenvironmentfromgenomicandmetagenomicdata |