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Toward the Augmentation of Ruminal Fermentation: Developing a Computational-Experimental Framework to Predict Microbiome Dynamics and Function

Project Team

  • Dr. Josh McCann, Assistant Professor, Dept. of Animal Sciences
  • Dr. Ting Lu, Associate Professor, Dept. of Bioengineering
  • Sara Tondini, Graduate Student, Dept. of Animal Sciences

Abstract

The gut microbial ecosystem is essential to the well-being of humans and livestock. However, it is difficult to harness as an effective tool to improve health and productivity. Although we can adequately characterize microbial composition, community functions and their interactions are poorly described. In addition, the sheer complexity of natural microbial communities complicates learning fundamental aspects of microbial interactions. Synthetic communities can be used as a model system to understand structure and function of natural communities with more defined control. These synthetic culture dynamics combined with computational modeling can provide a realistic means to understand more complex microbial systems. However, little work has been done to construct synthetic rumen communities and no work has been done to generate a model capable of predicting species level interactions in a rumen ecosystem.

Rumen microbiota play a crucial role in the nutritional capacity of ruminants as they are responsible for harvesting over 70% of the energy for the animal. Cellulolytic bacteria are specifically important in accessing the energy stored in plant cell wall biomass. This work proposes the first computational model of a cellulolytic rumen habitat at the strain-organism level. Our objective is to model and predict the functional capacity and metabolic relationships of rumen bacteria to improve efficiency of energy capture from cellulose fermentation. We will accomplish this objective by evaluating cellulolytic rumen bacteria in mono- and co-culture and using the experimental data will construct a computational framework to modularly predict more complex ecosystems. This model will elucidate the interactions observed among rumen microbiota, predict the dynamics of the full community, and identify optimal compositions to maximize cellulose degradation. The application of this technology to help understand gut microbiomes will improve animal health and efficiency, and thus contribute to sustainable animal production and provide nutritious, affordable food for human consumption.