Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

Abstract

We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can be used to analyze complex "omics" data and to infer and optimize metabolic processes. Thereby, SMN models are suitable to capitalize on advances in high-throughput molecular and metabolic data generation. SMN models are starting to be applied to describe microbial interactions during wastewater treatment, in-situ bioremediation, microalgae blooms methanogenic fermentation, and bioplastic production. Despite their current challenges, we envisage that SMN models have future potential for the design and development of novel growth media, biochemical pathways and synthetic microbial associations.

Keywords: environmental biotechnology; flux balance analysis; genome-scale metabolic model; metabolic network; microbial communities; process engineering; systems biology; wastewater treatment.

Figure 1

Pairwise microbial interactions in environmental processes. For each interaction partner, there are three possible outcomes: positive (+), negative (–), or neutral (0). Metabolic but not ecological interactions can be modeled using metabolic networks. Figure adapted from Großkopf and Soyer (2014).

Figure 2

Sub-models of an environmental system (e.g., a full scale wastewater treatment plant). SMN models are genome informed stoichiometric models of biological processes. Inherently, SMN is not a kinetic model therefore does not capture process dynamics. Nevertheless, SMN and kinetic models can be integrated in a common modeling framework.

Figure 3

The stoichiometric metabolic network modeling approach for analysis of microbial interactions and communities in natural and engineered environmental systems. The approach is subdivided in four main stages (i) sampling of microbial communities from environmental systems; (ii) characterization of community properties and species interactions through culture dependent and culture independent techniques; (iii) integration of experimental data through model development and analysis; and (iv) application of SMN model as tool to study basic mechanisms or design processes. DGGE, Denaturing Gradient Gel Electrophoresis; ARISA, Automated Ribosomal Intergenic Spacer Analysis; qPCR, quantitative Polymerase Chain Reaction; FISH, Fluorescence In-situ Hybridization. Dotted lines represent rounds of model calibration and validation against experimental data. The artwork representing the “Microbial community” was taken from Vanwonterghem et al. (2014).

Figure 4

Formulation of the stoichiometric metabolic network (SMN) of a single species (or microbial guild) using genomic information. DNA encodes information to synthesize specific proteins with enzymatic activities (A and B); proteins catalyze specific reactions where metabolites are used as substrates (x, a, y) to be transformed into products (z, b, c); subsequent reactions form metabolic pathways, which constitute cell metabolism; each reaction is represented as a stoichiometric equation (A and B); the equations are then compiled in an extensive list of reactions involved in the modeled pathways.

Figure 5

A research workflow to model microbial interactions using SMN models. (i) network reconstruction step; (ii) acquisition of experimental data; (iii) the model calibration step, which involves the statistical comparison of model estimated data against experimentally observed data and further model parameter adjustment to improve predictions; (iv) the calibrated model can be used to perform further analysis in other software platforms. See Section Computational Tools and Software for details.

Figure 6

Conceptual scheme of the four approaches to model mixed microbial cultures using stoichiometric metabolic networks. In all figures boxes S^A, S^B, S^C represent sets of equations (captured as an S matrix) of metabolic reactions occurring in organisms/guilds A, B, and C, respectively. S^ABC is a matrix lumping metabolic reactions occurring in organisms/guilds A, B, and C. These sets of reactions can have any number of sub compartments to model reactions occurring in the extracellular space and organelles; boxes with dashed lines indicate model (system) boundaries; boxes with solid lines indicate guild boundaries; v_j is the flux of metabolite in reaction j; V is the vector of fluxes estimated by the model; V^k is the vector of fluxes estimated by the model of species/guild k (A, B, or C); X_i is the concentration of metabolite i; μ^k (a.k.a. $v_{biomass}^k$) is the growth rate (biomass production rate) of species k; f^k is the fraction of species k in community's biomass; and $X_{biomass}^k$ is the biomass concentration of modeled species/guild k (A, B, or C). Figure inspired in Taffs et al. (2009) modeling approaches diagrams.