Mechanistic modelling of in vitro fermentation and methane production by rumen microbiota

Muñoz-Tamayo, Rafael; Giger-Reverdin, Sylvie; Sauvant, Daniel

doi:10.1016/j.anifeedsci.2016.07.005

Cited by 43 publications

(85 citation statements)

References 59 publications

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“…By including some extra bio‐chemical functions, this can be explicitly included in the system (e.g. Munoz‐Tamayo et al., ).…”

Section: Example Applicationsmentioning

confidence: 99%

“…Here we use microPop to model in vitro rumen fermentation, based on a simplified version of the model by Munoz‐Tamayo et al. (). The construction of this model is significantly different to the human colon model in Section in several ways.…”

Section: Example Applicationsmentioning

confidence: 99%

“… The rumen system based on the model by Munoz‐Tamayo et al. () consists of polymers (non‐structural carbohydrates ( Z nsc ), cell wall carbohydrates ( Z ndf ) and proteins ( Z pro ) which are hydrolised to the soluble components: sugars ( S su ) and amino acids ( S aa ). The microbial groups are sugar‐utilisers ( X su ), amino‐acid utilisers ( X aa ) and hydrogen utilisers ( X h 2 ).…”

Section: Example Applicationsmentioning

confidence: 99%

“…Using the same settings as Munoz‐Tamayo et al. (), we investigate how increasing the initial concentrations of the feed polymers, Z nsc , Z ndf and Z pro , affects the concentration of methane in the rumen ( S ch 4 ). Thus we set the initial polymer concentrations at 1 g/l and then increase each one in turn to 20 g/l (Figure ).…”

Section: Example Applicationsmentioning

confidence: 99%

“…Data frames for a number of MFGs found in the human large intestine (e.g. Bacteroides, Acetogens, Methanogens, Butyrate Producers, Lactate Producers and so on) as described by Kettle, Louis, Holtrop, Duncan, and Flint (2015) and the rumen (by Munoz-Tamayo, Giger-Reverdin, & Sauvant, 2016) are included in the package (Table 1). If the user simply wishes to use these MFGs, then microPop can be used "off the shelf," however, any number of other MFGs may also be added by the user by defining a data frame in the correct format.…”

Section: Introductionmentioning

confidence: 99%

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microPop: Modelling microbial populations and communities in R

et al. 2017

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Abstract1. Microbial communities perform highly dynamic and complex ecosystem functions that impact plants, animals and humans. Here we present an R-package, microPop, which is a dynamic model based on a functional representation of different microbiota.2. microPop simulates the dynamics and interactions of microbial populations by solving a system of ordinary differential equations that are constructed automatically based on a description of the system. 3. Data frames for a number of microbial functional groups (MFG) and default functions for rates of microbial growth, resource uptake, metabolite production are provided but can be modified or replaced by the user.4. microPop can simulate growth in a single compartment (e.g. bio-reactor) or "compartments" in series (e.g. human colon) or in a simple 1D application (e.g. phytoplankton in a water column). Furthermore, an MFG may contain multiple strains in order to study adaptation and diversity or parameter uncertainty. Also simple interactions between viruses (bacteriophages) and bacteria can be included in microPop.5. microPop is hosted on CRAN and can be installed directly from within R. This paper describes version 1.3 of microPop. The code is also hosted on GitHub for future development (https://github.com/HelenKettle/microPop). K E Y W O R D Sbacteria, modelling bacteriophages, modelling colonic microbiota, modelling methane production, modelling microbial diversity, modelling phytoplankton, ODEs, population dynamics | INTRODUCTIONMicrobial communities play a crucial role in bio-geochemical cycling and perform ecosystem functions important to plants, animals and humans. Building predictive models that link microbial community composition to function is a key emerging challenge in microbial ecology (Widder et al., 2016). Here, we present microPop, an R package which is a mechanistic model using ordinary differential equations (t)X(t)

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“…By including some extra bio‐chemical functions, this can be explicitly included in the system (e.g. Munoz‐Tamayo et al., ).…”

Section: Example Applicationsmentioning

confidence: 99%

Section: Example Applicationsmentioning

confidence: 99%

Section: Example Applicationsmentioning

confidence: 99%

Section: Example Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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microPop: Modelling microbial populations and communities in R

et al. 2017

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Thermodynamic approach to estimating reactions and stoichiometric coefficients of anaerobic glucose and hydrogen utilization

Wresta

Widyarani

Boopathy

et al. 2021

Engineering Reports

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Anaerobic digestion plays an important role in the gastrointestinal tract and in organic waste treatment. Thermodynamic analysis based on the reaction Gibbs free energy can be used to predict the favorability of some reactions occurring during anaerobic digestion. In this study, we used a thermodynamic approach to evaluating reactions and stoichiometric coefficients of the anaerobic process of in vitro rumen microbiota. The favorability of glucose, butyrate, propionate, and hydrogen utilizations was analyzed by calculating the Gibbs free energy change of each reaction. A previously published Gibbs free energy dissipation method was also used to calculate stoichiometric coefficients of the total metabolism reaction of glucose and hydrogen utilization. For glucose utilization in which the metabolism follows several different pathways, the fraction of glucose following each pathway is estimated by considering the number of electron transfer attributed throughout the catabolism reaction. Glucose utilization always occurs in the system, and the syntrophic correlation among butyrate, propionate, and hydrogen utilizations run well with propionate utilization following the alternative pathway that yields lower hydrogen. The approach applied in this research significantly reduces the stoichiometric coefficients that must be predicted in kinetic modeling. To verify the calculation result, the yield coefficients obtained were then applied in the previous mechanistic model of in vitro rumen microbiota, and the results were compared to the experimental data from literature.

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Mathematical modelling supports the existence of a threshold hydrogen concentration and media-dependent yields in the growth of a reductive acetogen

Smith

Shorten

Altermann

et al. 2020

Bioprocess Biosyst Eng

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The bacterial production of acetate via reductive acetogenesis along the Wood-Ljungdahl metabolic pathway is an important source of this molecule in several environments, ranging from industrial bioreactors to the human gastrointestinal tract. Here, we contributed to the study of reductive acetogens by considering mathematical modelling techniques for the prediction of bacterial growth and acetate production. We found that the incorporation of a hydrogen uptake concentration threshold into the models improves their predictions and we calculated this threshold as 86.2 mM (95% confidence interval 6.1-132.6 mM). Monod kinetics and first-order kinetics models, with the inclusion of two candidate threshold terms or reversible Michaelis-Menten kinetics, were compared to experimental data and the optimal formulation for predicting both growth and metabolism was found. The models were then used to compare the efficacy of two growth media for acetogens. We found that the recently described general acetogen medium was superior to the DSMZ medium in terms of unbiased estimation of acetogen growth and investigated the contribution of yeast extract concentration to acetate production and bacterial growth in culture. The models and their predictions will be useful to those studying both industrially and environmentally relevant reductive acetogenesis and allow for straightforward adaptation to similar cases with different organisms.

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Mechanistic modelling of in vitro fermentation and methane production by rumen microbiota

Cited by 43 publications

References 59 publications

microPop: Modelling microbial populations and communities in R

microPop: Modelling microbial populations and communities in R

Thermodynamic approach to estimating reactions and stoichiometric coefficients of anaerobic glucose and hydrogen utilization

Mathematical modelling supports the existence of a threshold hydrogen concentration and media-dependent yields in the growth of a reductive acetogen

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