There is still no satisfactory understanding of the factors that enable soil microbial populations to be as highly biodiverse as they are. The present article explores in silico the hypothesis that the heterogeneous distribution of soil organic matter, in addition to the spatial connectivity of the soil moisture, might account for the observed microbial biodiversity in soils. A multi-species, individual-based, pore-scale model is developed and parameterized with data from 3 Arthrobacter sp. strains, known to be, respectively, competitive, versatile, and poorly competitive. In the simulations, bacteria of each strain are distributed in a 3D computed tomography (CT) image of a real soil and three water saturation levels (100, 50, and 25%) and spatial heterogeneity levels (high, intermediate, and low) in the distribution of the soil organic matter are considered. High and intermediate heterogeneity levels assume, respectively, an amount of particulate organic matter (POM) distributed in a single (high heterogeneity) or in four (intermediate heterogeneity) randomly placed fragments. POM is hydrolyzed at a constant rate following a first-order kinetic, and continuously delivers dissolved organic carbon (DOC) into the liquid phase, where it is then taken up by bacteria. The low heterogeneity level assumes that the food source is available from the start as DOC. Unlike the relative abundances of the 3 strains, the total bacterial biomass and respiration are similar under the high and intermediate resource heterogeneity schemes. The key result of the simulations is that spatial heterogeneity in the distribution of organic matter influences the maintenance of bacterial biodiversity. The least competing strain, which does not reach noticeable growth for the low and intermediate spatial heterogeneities of resource distribution, can grow appreciably and even become more abundant than the other strains in the absence of direct competition, if the placement of the resource is favorable. For geodesic distances exceeding 5 mm, microbial colonies cannot grow. These conclusions are conditioned by assumptions made in the model, yet they suggest that microscale factors need to be considered to better understand the root causes of the high biodiversity of soils.

The factors leading to changes in the organization of microbial assemblages at fine spatial scales are not well characterized or understood. However, they are expected to guide the succession of community development and function toward specific outcomes that could impact human health and the environment. In this study, we put forward a combined experimental and agent-based modeling framework and use it to interpret unique spatial organization patterns of H1-Type VI secretion system (T6SS) mutants of P. aeruginosa under spatial confinement. We find that key parameters, such as T6SS-mediated cell contact and lysis, spatial localization, relative species abundance, cell density and local concentrations of growth substrates and metabolites are influenced by spatial confinement. The model, written in the accessible programming language NetLogo, can be adapted to a variety of biological systems of interest and used to simulate experiments across a broad parameter space. It was implemented and run in a high-throughput mode by deploying it across multiple CPUs, with each simulation representing an individual well within a high-throughput microwell array experimental platform. The microfluidics and agent-based modeling framework we present in this paper provides an effective means by which to connect experimental studies in microbiology to model development. The work demonstrates progress in coupling experimental results to simulation while also highlighting potential sources of discrepancies between real-world experiments and idealized models.

Macroscopic models of soil organic matter (SOM) turnover have faced difficul-

Yeast ageing and inoculum size are factors that affect successive industrial fermentation, particularly in those processes that reuse the yeast cells. The aim of the work is to explore the effects of inocula size and aging on the dynamics of yeast population. However, only individual-based modelling (IbM) makes possible studies of small, well characterized, microbial inocula. Here we have made use of INDISIM-YEAST to carry out these studies.Several simulations were performed to analyze inoculum size and their different genealogical ages on the lag phase, first division time and specific growth rate. Shortest lag phase and time to the first division where obtained with largest inocula and with youngest inoculated parent cells.

Purpose Simultaneously interacting rhizosphere processes determine emergent plant behaviour, including growth, transpiration, nutrient uptake, soil carbon storage and transformation by microorganisms. However, these processes occur on multiple scales, challenging modelling of rhizosphere and plant behaviour. Current advances in modelling and experimental methods open the path to unravel the importance and interconnectedness of those processes across scales. Methods We present a series of case studies of state-of-the art simulations addressing this multi-scale, multi-process problem from a modelling point of view, as well as from the point of view of integrating newly available rhizosphere data and images. Results Each case study includes a model that links scales and experimental data to explain and predict spatial and temporal distribution of rhizosphere components. We exemplify the state-of-the-art modelling tools in this field: image-based modelling, pore-scale modelling, continuum scale modelling, and functional-structural plant modelling. We show how to link the pore scale to the continuum scale by homogenisation or by deriving effective physical parameters like viscosity from nano-scale chemical properties. Furthermore, we demonstrate ways of modelling the links between rhizodeposition and plant nutrient uptake or soil microbial activity. Conclusion Modelling allows to integrate new experimental data across different rhizosphere processes and scales and to explore more variables than is possible with experiments. Described models are tools to test hypotheses and consequently improve our mechanistic understanding of how rhizosphere processes impact plant-scale behaviour. Linking multiple scales and processes including the dynamics of root growth is the logical next step for future research.

The yeast Saccharomyces cerevisiae has a limited replicative lifespan. The cell mass at division is partitioned unequally between a larger, old parent cell and a smaller, new daughter cell. Industrial beer fermentations maintain and reuse yeast. At the end of fermentation a portion of the yeast is 'cropped' from the vessel for 'serial repitching'. Harvesting yeast may select a population with an imbalance of young and aged individuals, but the output of any bioprocess is dependent on the physiology of each single cell in the population. Unlike continuous models, individual-based modelling is an approach that considers each microbe as an individual, a unique and discrete entity, with characteristics that change throughout its life. The aim of this contribution is to explore, by means of individual-based simulations, the effects of inoculum size and cell genealogical age on the dynamics of virtual yeast fermentation, focussing on: (1) the first stages of population growth, (2) the mean biomass evolution of the population, (3) the rate of glucose uptake and ethanol production, and (4) the biomass and genealogical age distributions. The ultimate goal is to integrate these results in order to make progress in the understanding of the composition of yeast populations and their temporal evolution in beer fermentations. Simulation results show that there is a clear influence of these initial features of the inocula on the subsequent growth dynamics. By contrasting both the individual and global properties of yeast cells and populations, we gain insight into the interrelation between these two types of data, which helps us to deal with the macroscopic behaviour observed in experimental research.

Individual-based models (IBMs), the biological agentbased models, are currently being applied to the study of microbial systems. A microbial IBM of yeast populations growing in liquid bath cultures has already been designed and implemented in the simulator called INDISIM-YEAST. In order to improve its predictive capabilities and further its development, a deeper understanding of how the variation of the output of the model can be apportioned, qualitatively or quantitatively, to different sources of variation must be investigated. The aim of this study is to show how insights into the individual cell parameters of INDISIM-YEAST can be obtained combining local and global methods using classic and well-proven methods, and to illustrate how these simple methods provide useful, reliable results with this IBM. This work deals mainly with the use of screening methods, as the main task to perform here is that of identifying the most influential factors for this microbial IBM. This screening exercise has allowed the establishment of significant input factors to this IBM on yeast population growth, and the highlighting of those that require greater attention in the parameterization and calibration processes.

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