Systems modelling of microbial communities using in vitro and computational approaches

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Understanding and predicting how microbial communities react to changes in their environment is critical for the development of microbiome-based applications. There are many fields where being able to control the composition and activity of microbiomes would be beneficial, including biotechnology, bioremediation and animal/human nutrition. Previous studies suggest that by knowing the composition of a microbiome, we could predict its response to specific interventions. In vitro models of microbial communities provide useful systems for the high-throughput study of the impact of molecules on microbiomes. It has been shown previously that in vitro microbiomes allowed discoveries that were replicated in animal models. These approaches are also able to cost-effectively provide a large quantity of information that is necessary to model microbial communities with machine learning. To study the microbiome, scientists sequence the genomes of bacterial communities. This provides large quantities of DNA sequences that must be carefully interpreted. In most studies, researchers quantify the abundance of the taxonomical origin of bacteria and determine the metabolic functions of the genes encoded in bacterial genomes. However, only 35% to 45% of genes from the gut microbiome can be associated with actual functions. This often limits the analysis and interpretation of microbiome-related studies and overlooks the extensive functional and ecological possibilities of microbial communities. In this research program, we aim to create the basis of a framework for the targeted modulation of microbial communities. To do so, we will address two critical aspects that need to be resolved before we can deliberately modulate microbiomes to get specific effects. First, we will devise new ways to represent microbiomes in a manner that allows to efficiently consider all the genes and bacterial species, including those with unknown functions. We will use machine learning to discover meaningful patterns in the data that may be overlooked using current methods based on bacteria quantification and gene function profiling. Second, we will use machine learning to predict the response of the microbiome to specific conditions and to determine the best sequence of interventions to obtain a desired microbiome effect. We will use in vitro culture of stool samples in presence of three trace minerals as a model to develop new methods to make possible targeted microbiome modulation. The use of machine learning to model the response of microbiomes to their chemical environment will permit a deeper understanding of the interplay between microorganisms and their environment. Overall, our research program will provide methods to modulate microbiomes that will be applicable to many fields, including biotechnology and personalized nutrition for both humans and animals. This project will also provide specific guidelines to design studies to optimize microbial communities using machine learning.