365 Towards the Integration of Microbial Genomic Information Into Mechanistic Models of the Rumen Microbiome: A Theoretical Study

Abstract

Abstract The objective of this work was to develop a mathematical framework enabling the integration of microbial genomic information (i.e., 16S rDNA) into mechanistic models of the rumen microbiome. Mechanistic modelling provides promising tools to enhance our system-level understanding of the rumen ecosystem. Existing mechanistic models of rumen fermentation consider an aggregated representation of the rumen microbiota and its metabolic function. However, none of these models integrate microbial genomic knowledge and are thus limited to capitalize on the rich information of microbial genomic sequencing. In this work, we investigated theoretically, how microbial time series of the rumen microbiota determined by 16S rDNA can be integrated into a mechanistic fermentation model of the rumen microbiome. Our study used a previously developed model of in vitro rumen fermentation that represents the microbiota by three microbial functional groups, namely sugar utilisers, amino acid utilisers, and methanogens 1. The model was extended to represent the fermentation under continuous operation (i.e. chemostat). We considered a hypothetical scenario where 16S rumen microbial time series are available. Our rationale is that the microbial species can be categorized into the macroscopic functions of the mechanistic model by using tools such as CowPI 2. In a simulation case study, we used the theory of state observers to integrate the mechanistic model and the microbial data to provide estimations of the dynamics of the volatile fatty acids (VFA) acetate, butyrate, and propionate. The estimated VFA converge towards the real VFA dynamics demonstrating the promising potential of our approach. The next step we are currently working is the validation of our approach using in vitro experimental data. 1 Muñoz-Tamayo, R. et al. Anim. Feed Sci. Technol. 220, 1–21 (2016)2 Wilkinson, T. J. et al. Front. Microbiol. 9, 1095 (2018)