From Network Analysis to Functional Metabolic Modeling of the Human Gut Microbiota

Abstract

An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.

Keywords: computational modeling; constraint-based modeling; gut microbiome; metabolic modeling; network approaches.

FIG 1

Examples of data-driven network reconstruction based on high-throughput data from different patients or conditions (A) and knowledge-driven networks based on genome-scale metabolic reconstructions (B).

FIG 2

Flux balance simulations of individual genome-scale metabolic models and the emergence of alternative optimal flux distributions.

FIG 3

Flux balance simulation (FBA) of compartment-based communities (A) as well as dynamic FBA of population-based (B) and individual-based models (C).