Systems modeling approaches for microbial community studies: from metagenomics to inference of the community structure

Abstract

Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call "the community state", that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist.

Keywords: community modeling; flux balance analysis; genome-scale stoichiometric modeling; metabolism; metagenomic data integration; microbial communities.

Figure 1

Illustration of the diversity of a microbial ecosystem with various interactions. (A) Is a schematic representation of an ecosystem with interactions among community members and the environment. The small particles are the metabolites, the big particles the community members, the boxes show the different interactions between community members. (B) Shows an example time series data set of the relative biomass abundances in this ecosystem. (C) Shows the real dynamics of the various species of the ecosystem in time. The mechanisms behind the dynamics cannot be captured by metagenomics time series data alone.

Figure 2

Illustration of FBA (A). Visualization of a simplified metabolic network of a micro-organism. The microorganism takes up metabolite A and produces biomass, and products D and E. (B) The stoichiometric matrix N representing the network depicted in A, with rows corresponding to metabolites and columns to fluxes. The stoichiometric matrix multiplied with the flux vector is in steady-state always 0. (C) When optimization of the biomass flux is used, the (in)feasible flux distribution figure between flux v1 and v3 is calculated. The red dot corresponds with the optimal solution when the biomass flux is used as the objective function.

Figure 3

Representation of the work flow to get more information from experimental data. On one hand the inference approach with as result a coarse-grained data-model which fits the measured data and on the other hand the genome-scale models which are highly underdetermined, but can be used as data repositories. The challenge is to create models which fit the research question. This can be done via the represented steps.

Figure 4

Illustration of the models used to get a mechanistic understanding of the nitrate respiring community studied by Kraft et al. (2014). At the top the coarse-grained models, whereas at the bottom the highly detailed genome-scale models are shown. In the middle is the type of model created using the synergistic approach of the coarse-grained models and the genome-scale models which will contribute to mechanistic understanding of the nitrate respiring community and it's functioning.