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

Muñoz-Tamayo, R.; Davoudkhani, M.; Fakih, I.; Robles-Rodriguez, C.E.; Rubino, F.; Creevey, C.J.; Forano, E.

doi:10.1016/j.animal.2023.100984

Cited by 3 publications

(2 citation statements)

References 72 publications

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“…However, none of these models integrate microbial genomic knowledge and thus do not capitalize on the rich information that microbial genomic sequencing provides. Integration of dynamic modelling and microbial data has the potential to improve the understanding of the rumen ecosystem, to enhance predictive power of rumen models and to help the design of microbial manipulation strategies to improve rumen function [17]. Recently, some studies have applied the genomescale metabolic approach to reconstruct metabolic networks of rumen microbes species [18][19][20] and to predict the metabolism of minimal rumen microbial consortium [21].…”

Section: Introductionmentioning

confidence: 99%

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling

Davoudkhani,

Rubino,

Creevey

et al. 2024

PLoS ONE

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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 an in vitro RUSITEC experiment and from an in vivo experiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For the in vitro case study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For the in vivo case 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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Section: Introductionmentioning

confidence: 99%

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling

Davoudkhani,

Rubino,

Creevey

et al. 2024

PLoS ONE

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, none of these models integrate microbial genomic knowledge and thus do not capitalize on the rich information that microbial genomic sequencing provides. Integration of dynamic modelling and microbial data has the potential to improve the understanding of the rumen ecosystem, to enhance predictive power of rumen models and to help the design of microbial manipulation strategies to improve rumen function (Muñoz-Tamayo et al, 2023). Recently, some studies have applied the genome-scale metabolic approach to reconstruct metabolic networks of rumen microbes species (Fakih et al, 2023; Lee et al, 2020; Pereira et al, 2018) and to predict the metabolism of minimal rumen microbial consortium (Islam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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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Editorial: The 10th international Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT)

Cannas,

Cabrera,

Dougherty

et al. 2023

animal

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

Cited by 3 publications

References 72 publications

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling

Integrating microbial abundance time series with fermentation dynamics of the rumen microbiome via mathematical modelling

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

Editorial: The 10th international Workshop on Modelling Nutrient Digestion and Utilization in Farm Animals (MODNUT)

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