Kettle, H. | Search bioRxiv

bioRxiv2022

Process-based modelling of microbial community dynamics in the human colon

The human colon contains a dynamic microbial community whose composition has important implications for human health. In this work we build a process-based model of the colonic microbial ecosystem and compare with general empirical observations and the results of in-vivo experiments. Based our previous work (Kettle et al., 2015), the microbial model consists of 10 microbial functional groups, 4 substrates and 10 metabolites; to this we add the interaction with a human host to give simulations of the in-situ colonic microbial ecosystem. This model incorporates absorption of short chain fatty acids (SCFA) and water by the host through the gut wall, variations in incoming dietary substrates (in the form of "meals" whose composition varies in time), bowel movements, feedback on microbial growth from changes in pH resulting from SCFA production, and multiple compartments to represent the proximal, transverse and distal colon. We verify our model against a number of observed criteria, e.g. total SCFA concentrations, SCFA ratios, mass of bowel movements, pH and water absorption over the transit time; and then run simulations investigating the effect of colonic transit time, and the composition and amount of indigestible carbohydrate in the host diet, which we compare with in-vivo studies. Gut microbiota are highly complex and poory understood yet our work shows that it is nevertheless possible to develop predictive models of the key components of the dynamics of this ecological system. The code is available as an R package (microPopGut) to aid future research. Author SummaryKettle wrote the model code and led the writing of the manuscript. Louis and Flint both contributed to writing the manuscript and all aspects of microbiolgy. All authors contributed critically to the drafts and gave final approval for publication.

microbiology↗

bioRxiv2018

A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle

Large efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations. Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). DMI and IT were recorded with load cells. A total of 37 individual dynamic patterns of methane production were analysed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lins concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. Since IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.\n\nImplicationsReducing methane emissions from ruminants is a major target for sustainable and efficient livestock farming. For the animal, methane production represents a loss of feed energy. For the environment, methane exerts a potent greenhouse effect. Methane mitigation strategies require accurate, non-invasive and inexpensive techniques for estimating individual methane emissions on farm. In this study, we integrate measurements of feeding behaviour in cattle and a mathematical model to estimate individual methane production. Together, model and measurements form a software sensor that efficiently predicts methane output. Our software sensor is a promising approach for estimating methane emissions at large scale.

bioengineering↗