Xavier Portell

Individual-based modeling of carbon and nitrogen dynamics in soils:: parameterization and sensitivity analysis of abiotic components

The need to predict with reasonable accuracy the fate of soil C and N compounds in soils in response to climate change is stimulating interest in a new generation of microscale models of soil ecosystem processes. Essential to the development of such models is the ability to describe the growth and metabolism of small numbers of individual microorganisms. In this context, the key objective of the research described in this article was to further develop an individual-based soil organic matter (SOM) model, INDISIM-SOM, first proposed a few years ago, and to assess its performance with a broader data set than previously considered. The INDISIM-SOM models the dynamics and evolution of C and N associated with organic matter in soils. The model involves a number of state variables and parameters related to SOM and microbial activity, including growth and decay of microbial biomass, temporal evolution of mineralized intermediate C and N, mineral N in ammonium and nitrate, carbon dioxide, and O2. Simulation results demonstrate good fit of the model to experimental data from laboratory incubation experiments performed on three different types of Mediterranean soils. A second objective was to determine the sensitivity of the model toward its various parameters. Sensitivity was small for several of the parameters, suggesting possible simplifications of the model for specific uses, but was significant particularly for the parameter associated with the fraction of the soil C present in the biomass. These results suggest that research should be focused on improving the measurement of this latter parameter.

Analysis of the effect of inoculum characteristics on the first stages of a growing yeast population in beer fermentations by means of an individual-based model.

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.

Population analysis of a commercial Saccharomyces cerevisiae wine yeast in a batch culture by electric particle analysis, light diffraction and flow cytometry

Data from electric particle analysis, light diffraction and flow cytometry analysis provide information on changes in cell morphology. Here, we report analyses of Saccharomyces cerevisiae populations growing in a batch culture using these techniques. The size distributions were determined by electric particle analysis and by light diffraction in order to compare their outcomes. Flow cytometry parameters forward (related to cell size) and side (related to cell granularity) scatter were also determined to complement this information. These distributions of yeast properties were analysed statistically and by a complexity index. The cell size of Saccharomyces at the lag phase was smaller than that at the beginning of the exponential phase, whereas during the stationary phase, the cell size converged with the values observed during the lag phase. These experimental techniques, when used together, allow us to distinguish among and characterize the cell size, cell granularity and the structure of the yeast population through the three growth phases. Flow cytometry patterns are better than light diffraction and electric particle analysis in showing the existence of subpopulations during the different phases, especially during the stationary phase. The use of a complexity index in this context helped to differentiate these phases and confirmed the yeast cell heterogeneity.

A Microfluidics and Agent-Based Modeling Framework for Investigating Spatial Organization in Bacterial Colonies: The Case of Pseudomonas Aeruginosa and H1-Type VI Secretion Interactions

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.

Microscale Heterogeneity of the Spatial Distribution of Organic Matter Can Promote Bacterial Biodiversity in Soils: Insights From Computer Simulations

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.

Population analysis of a commercial Saccharomyces cerevisiae wine yeast in a batch culture by electric particle analysis, light diffraction and flow cytometry: Distributional analysis of S. cerevisiae in batch culture

Data from electric particle analysis, light diffraction and flow cytometry analysis provide information on changes in cell morphology. Here, we report analyses of Saccharomyces cerevisiae populations growing in a batch culture using these techniques. The size distributions were determined by electric particle analysis and by light diffraction in order to compare their outcomes. Flow cytometry parameters forward (related to cell size) and side (related to cell granularity) scatter were also determined to complement this information. These distributions of yeast properties were analysed statistically and by a complexity index. The cell size of Saccharomyces at the lag phase was smaller than that at the beginning of the exponential phase, whereas during the stationary phase, the cell size converged with the values observed during the lag phase. These experimental techniques, when used together, allow us to distinguish among and characterize the cell size, cell granularity and the structure of the yeast population through the three growth phases. Flow cytometry patterns are better than light diffraction and electric particle analysis in showing the existence of subpopulations during the different phases, especially during the stationary phase. The use of a complexity index in this context helped to differentiate these phases and confirmed the yeast cell heterogeneity.

Sensitivity Analysis And Individual-Based Models In The Study Of Yeast Populations.

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 INDISIMYEAST 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.