Abstract
Natural soil is characterized as a complex habitat with patchy hydrated islands and spatially variable nutrients that is in a constant state of change due to wetting-drying dynamics. Soil microbial activity is often concentrated in sparsely distributed hotspots that contribute disproportionally to macroscopic biogeochemical nutrient cycling and greenhouse gas emissions. The mechanistic representation of such dynamic hotspots requires new modeling approaches capable of representing the interplay between dynamic local conditions and the versatile microbial metabolic adaptations. We have developed IndiMeSH (Individual-based Metabolic network model for Soil Habitats) as a spatially explicit model for the physical and chemical microenvironments of soil, combined with an individual-based representation of bacterial motility and growth using adaptive metabolic networks. The model uses angular pore networks and a physically based description of the aqueous phase as a backbone for nutrient diffusion and bacterial dispersal combined with dynamic flux balance analysis to calculate growth rates depending on local nutrient conditions. To maximize computational efficiency, reduced scale metabolic networks are used for the simulation scenarios and evaluated strategically to the genome scale model. IndiMeSH was compared to a well-established population-based spatiotemporal metabolic network model (COMETS) and to experimental data of bacterial spatial organization in pore networks mimicking soil aggregates. IndiMeSH was then used to strategically study dynamic response of a bacterial community to abrupt environmental perturbations and the influence of habitat geometry and hydration conditions. Results illustrate that IndiMeSH is capable of representing trophic interactions among bacterial species, predicting the spatial organization and segregation of bacterial populations due to oxygen and carbon gradients, and provides insights into dynamic community responses as a consequence of environmental changes. The modular design of IndiMeSH and its implementation are adaptable, allowing it to represent a wide variety of experimental and in silico microbial systems.
Highlights
Soil hosts the greatest diversity and abundance of microbial life in all of the biosphere, with over 109 cells per gram of soil [1] expressing thousands to millions of different operational taxonomic units in small volumes [2,3]
As soil opacity and complexity limit detailed observations of such hotspots in situ, we have developed a modeling platform, IndiMeSH (Individual-based Metabolic network model for Soil Habitats), to enable systematic study of dense multispecies bacterial communities within a structured habitat resembling to soil
Individual-based models (IBM) have been used to investigate a range of questions in microbial ecology such as altruism in biofilms [14], the influence of metabolic switching on biofilm structure [15], bacterial coexistence due to hydration dynamics [16], bacterial spatial organization in soil aggregates [17], and the dynamics of bacterial community diversity in desert soils [18]
Summary
Soil hosts the greatest diversity and abundance of microbial life in all of the biosphere, with over 109 cells per gram of soil [1] expressing thousands to millions of different operational taxonomic units in small volumes [2,3]. Considering the vast surface area per volume of a typical soil, bacterial cells are sparsely distributed within the soil matrix, covering less than 1% of the total available soil surface [4,5]. This highly non-uniform distribution is further reinforced by localized resource distribution giving rise to microbial hotspots, such as in soil aggregates, the rhizosphere, biopores or the detritusphere [6]. Individual-based models (IBM) have gained in popularity owing to their local scale and cell-level representation of interactions, which offer a useful platform for prediction of emergent spatial patterns and population abundances arising from microscale interactions. A flexible approach to calculating bacterial cell response to local conditions and potential switching of metabolic strategies became available with the solution of metabolic networks that make no prior assumptions about growth rates [19]
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