52. Integration of microbial time series into a mechanistic model of the rumen microbiome under the Rusitec condition

Davoudkhani, M.; Rubino, F.; Creevey, Christopher J.; Belanche, Alejandro; Muñoz-Tamayo, Rafael

doi:10.1016/j.anscip.2022.07.443

Cited by 4 publications

(9 citation statements)

References 4 publications

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“…We used the capabilities of CowPI (Wilkinson et al, 2018) to infer the microbial function of microbial time series based on 16S data. Our results indicated the promising application of observers and microbial time series data to investigate alternatives to connect omic data and mathematical modelling for studying the rumen microbial ecosystem (Davoudkhani et al, 2022b).…”

Section: Microbial Time Series and State Observersmentioning

confidence: 81%

18. Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

Muñoz-Tamayo

Davoudkhani²,

Fakih³

et al. 2022

Animal - science proceedings

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Section: Microbial Time Series and State Observersmentioning

confidence: 81%

18. Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

Muñoz-Tamayo

Davoudkhani²,

Fakih³

et al. 2022

Animal - science proceedings

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“…The scripts for the inference of the functional microbial modules are available at https://github.com/frubino/cowpi and https://doi.org/10.5281/zenodo.8401851. The abundances of each module for each case study are in the Tables modules.Rusitec.xls and modules.Cows in (Davoudkhani et al, 2023). A total of 308 modules were identified.…”

Section: Resultsmentioning

confidence: 99%

“…The microbial proxies for VFA production are named as M00579 ( m ac ), M99999 ( m bu ), M00013 ( m pr ). The implementation of the observer for each case study is available at (Davoudkhani et al, 2023).…”

Section: Resultsmentioning

confidence: 99%

“…The model performance was assessed by computing the coefficient of variation of the root mean squared error (CVRMSE). The Matlab scripts are available at (Davoudkhani et al, 2023) https://doi.org/10.5281/zenodo.8386786.…”

Section: Methodsmentioning

confidence: 99%

“…The scripts for the inference of the functional modules are available at https://github.com/frubino/cowpi and https://doi.org/10.5281/zenodo.8401851. The data and scripts of the implementation of the observer for each case study are available at (Davoudkhani et al, 2023).…”

Section: Availability Of Data and Materialsmentioning

confidence: 99%

See 2 more Smart Citations

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiomeviamathematical modelling

Davoudkhani,

Rubino,

Creevey

et al. 2023

Preprint

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The rumen represents a dynamic microbial ecosystem where fermentation metabolites and microbial concentrations change over time in response to dietary changes. The integration of microbial genomic knowledge and dynamic modelling can enhance our system-level understanding of rumen ecosystem’s function. However, such an integration between dynamic models and rumen microbiota data is lacking. The objective of this work was to integrate rumen microbiota time series determined by 16S rRNA gene amplicon sequencing into a dynamic modelling framework to link microbial data to the dynamics of the volatile fatty acids (VFA) production during fermentation. For that, we used the theory of state observers to develop a model that estimates the dynamics of VFA from the data of microbial functional proxies associated with the specific production of each VFA. We determined the microbial proxies using CowPi to infer the functional potential of the rumen microbiota and extrapolate their functional modules from KEGG (Kyoto Encyclopedia of Genes and Genomes). The approach was challenged using data from anin vitroRUSITEC experiment and from anin vivoexperiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For thein vitrocase study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For thein vivocase study, the mean CVRMSE were 16.4% for acetate, 15.8% for butyrate and 19.8% for propionate. The mean CVRMSE for the VFA molar fractions were 3.1% for acetate, 3.8% for butyrate and 8.9% for propionate. Ours results show the promising application of state observers integrated with microbiota time series data for predicting rumen microbial metabolism.

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Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

Muñoz-Tamayo,

Davoudkhani,

Fakih

et al. 2023

animal

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52. Integration of microbial time series into a mechanistic model of the rumen microbiome under the Rusitec condition

Cited by 4 publications

References 4 publications

18. Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

18. Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiomeviamathematical modelling

Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies

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