In silico approaches to study mass and energy flows in microbial consortia: a syntrophic case study

Abstract

BackgroundThree methods were developed for the application of stoichiometry-based network analysis approaches including elementary mode analysis to the study of mass and energy flows in microbial communities. Each has distinct advantages and disadvantages suitable for analyzing systems with different degrees of complexity and a priori knowledge. These approaches were tested and compared using data from the thermophilic, phototrophic mat communities from Octopus and Mushroom Springs in Yellowstone National Park (USA). The models were based on three distinct microbial guilds: oxygenic phototrophs, filamentous anoxygenic phototrophs, and sulfate-reducing bacteria. Two phases, day and night, were modeled to account for differences in the sources of mass and energy and the routes available for their exchange.ResultsThe in silico models were used to explore fundamental questions in ecology including the prediction of and explanation for measured relative abundances of primary producers in the mat, theoretical tradeoffs between overall productivity and the generation of toxic by-products, and the relative robustness of various guild interactions.ConclusionThe three modeling approaches represent a flexible toolbox for creating cellular metabolic networks to study microbial communities on scales ranging from cells to ecosystems. A comparison of the three methods highlights considerations for selecting the one most appropriate for a given microbial system. For instance, communities represented only by metagenomic data can be modeled using the pooled method which analyzes a community's total metabolic potential without attempting to partition enzymes to different organisms. Systems with extensive a priori information on microbial guilds can be represented using the compartmentalized technique, employing distinct control volumes to separate guild-appropriate enzymes and metabolites. If the complexity of a compartmentalized network creates an unacceptable computational burden, the nested analysis approach permits greater scalability at the cost of more user intervention through multiple rounds of pathway analysis.

Characterization and modelling of interspecies electron transfer mechanisms and microbial community dynamics of a syntrophic association

“…1b). Third, to model the metabolic interactions, we integrated these two genome-scale models using an extension of the compartmentalized modelling approach [11][12][13] . This integrated genome-scale model of syntrophy that consists of 1,824 genes and 2,333 reactions in total, and five compartments, where each species was assumed to have independent cytosol and periplasm compartments.…”

“…Recently, several COBRA methods have been deployed to investigate the various microbial interactions such as mutualism, competition and parasitism [10][11][12][13][14][15] . One of the first such modelling studies applied flux balance analysis to investigate the mutualism between the sulphate-reducing bacterium Desulfovilbrio vulgaris and the methanogenic archaeon Methanococcus maripaludis 15 .…”

Photorespiration and Rate Synchronization in a Phototroph-Heterotroph Microbial Consortium

“…The valuation η = 1 corresponds to all fixed carbon being used for a growth pathway, while η < 1 indicates that some of the fixed carbon is instead allocated to a pathway that results in secretion of organic carbon from the cell (with η = 0 corresponding to all fixed carbon going to the secretion pathway, though this outcome would not allow a viable population). Within the model presented here, there are seemingly two apparent advantages to η < 1 over η = 1: first, η < 1 results in consumption of oxygen, see (8), which may alleviate effects of oxygen oversupply, and second, η < 1 results in secreted organic carbon which can be used to supply a population of heterotrophs which in turn produce inorganic carbon as a byproduct. These points are discussed in more detail below, but note that we suppose here that the value of η is subject to control by cyanobacterial cells themselves.…”

“…An emphasis on rate rather than concentration is key and all internal reaction rates are effectively slaved to rates of inflow and outflow to/from the cell. The only other needed parameters are the stoichiometric ones, which are known from the pathway descriptions, in this case (7) and (8). Hence it is important to characterize governing rates, particularly those that have limiting or other important roles.…”

“…However, gene encoded functions relevant to metabolic processes are naturally organized into gene pathways [4][5][6], and then, under the often reasonable assumption of steady state, balance of influx and outflux through these pathways makes choice of individual reaction rates within any particular pathway unnecessary, replaceable instead by a single flux through that entire path. Regulatory function can be characterized as a management of resource allocation between different paths and then modeled by imposing optimality criteria on that allocation [7,8]. The result is an enormous simplification: cell function is now characterized by only a limited number of rates of cellular inflow and outflow of substrates and byproducts together with an optimization principle to divide them between available metabolic pathways.…”