Joshua Garcia and Jenny Kao-Kniffin

The microbiome can be thought of as the community of microbes that live on or within populations and groups of species and that potentially interact to influence the traits of a eukaryotic host or ecosystem.  In recent times, interest has grown in understanding how microbiomes associated with plants and animals influence the survivability or fitness of their hosts. The profound diversity of bacterial and fungal species found in certain environments provides a large pool of potentially hundreds of microbial partners that could interact in ways that influence host development. An example of host traits influenced by complex microbial interactions include microbial enhancement of metabolism in animal hosts through the production of bacterial enzymes that break down polysaccharides, polyphenols, and other substrates, aiding in host digestion and nutrient capture. An analog in plants would be the production of extracellular enzymes by a consortia of soil bacteria that mineralize nitrogen or phosphorus bound in organic matter into forms available for plant uptake. In some environments, including soil, the complexity of species diversity makes it challenging to assess the components of the microbiome that influence host traits. To address this knowledge gap, more robust tools are needed to identify interaction networks of microbiota that could signal an associative change in the development of plant or animal host traits. In this paper, we discuss applying mathematical tools to studies of microbiomes to identify potential alterations in microbial communities over time. Dynamic network modeling, which allows for the analysis of system dynamics over time, can aid in identifying alterations in microbiome composition associated with environmental or biological change. The resulting interaction networks of microbial taxa can be visualized as a phenotype of community behavior that could indicate increased coordination of the microbiome over time in parallel with host trait development or evolution.

Read the paper in full here. This paper is part of the Special Feature: Evolution and Ecology of Microbiomes.