Application of ecological network theory to the human microbiome

Abstract

In healthy humans, many microbial consortia constitute rich ecosystems with dozens to hundreds of species, finely tuned to functions relevant to human health. Medical interventions, lifestyle changes, and the normal rhythms of life sometimes upset the balance in microbial ecosystems, facilitating pathogen invasions or causing other clinically relevant problems. Some diseases, such as bacterial vaginosis, have exactly this sort of community etiology. Mathematical network theory is ideal for studying the ecological networks of interacting species that comprise the human microbiome. Theoretical networks require little consortia specific data to provide insight into both normal and disturbed microbial community functions, but it is easy to incorporate additional empirical data as it becomes available. We argue that understanding some diseases, such as bacterial vaginosis, requires a shift of focus from individual bacteria to (mathematical) networks of interacting populations, and that known emergent properties of these networks will provide insights that would be otherwise elusive.

Figure 1

A hypothetical trophic web with five species. Species 1 and 2 are “grazers” at the bottom level, which acquire nutrients directly from the environment and provide nutrients to species 3 and 5. Species 3, 4, and 5 form a dependent cycle, with 3 and 5 at the second level of the web and 4 at the final level.

Figure 2

Mathematical network with undirected edges, representing the structure of the trophic web in Figure 1, pictorially and as an equivalent adjacency matrix. The connectivity of the nodes is one for node 2, two for nodes 1 and 4, three for node 3, and four for node 5 (which is a hub node).

Figure 3

Directed graph representing (hypothetical) strengths of species interactions and the corresponding matrix of interaction strengths. Positive (negative) values indicate increase (decrease) in receiving species' fitness. Units of interaction are unspecified in this example, but may be observed changes in biomass. For example, species 1 may produce a metabolite beneficial to species 3(a ₃₁ = 1.2), while 3 occasionally harms 1(a ₁₃ = −0.3) while consuming the metabolite. Species 3 and 4 are competitors, 5 and 4 are mutualists, and other pairs resemble predator/prey.