, 2010, 2011) have enabled these models to be generated in a high-throughput manner for tens of thousands of microbial genomes. This approach is becoming increasingly relevant as draft quality genomes of the most abundant organisms in a microbial community can be assembled from metagenomic data (Woyke et al., 2010; Hess et al., 2011; Mackelprang et al., 2011; Iverson et al., 2012; Luo et al., 2012). In particular, CAL-101 nmr Mackelprang et al. (2011) found that the most abundant organism present in Alaskan permafrost soil was a novel methanogen and that modeling its metabolism from the assembled draft

genome provided direct insight into how the thawing permafrost will contribute methane, a powerful greenhouse gas, to the atmosphere. Microbial interaction models predict how the metabolisms of two or more microbial taxa interact with one another and their environment. Flux-balance models, which have been proven to be successful, are now being taken a step further to enable the development of simple interaction models between multiple individual flux-balance models for different genomes (Freilich et al., 2011). Individual-based models represent space as a discrete lattice, and each lattice element can contain microbial cells and measures of environmental

parameter levels. Each microbial cell in the GKT137831 datasheet model is an individual and can have various capacities to interact with environmental parameters (O’Donnell et al., 2007). Applying individual-based methods to entire microbial communities requires highly detailed, very accurate information about microbial metabolism and the nature of the microenvironment (Ferrer et al., 2008;

Freilich et al., 2011). Fortunately, there are computational techniques for describing multiphase transport in complex, porous media like soil, such as the Lattice-Boltzmann method (i.e. Zhang et al., 2005), which is a class of computational fluid dynamics techniques. Using these methods, it may be possible to model the dynamic movement of soil and then overlay this with biological information regarding the dynamics of the microbiome in that system; however, this has not yet been validated. Because this form Racecadotril of modeling can be computationally intensive, some methodological innovations, such as the use of superindividuals, have been advocated (Scheffer et al., 1995). The first study using individual-based modeling to predict the behavior of a microbial community simulated the accumulation of nitrate by nitrifying bacteria in different soil types (Ginovart et al., 2005). Recently, Gras et al. (2010) modeled the metabolism and dynamics of organic carbon and nitrogen in three different types of Mediterranean soil. The model incorporated specific parameters for growth and decay of microbial biomass, temporal evolution of mineralized intermediate carbon and nitrogen, mineral nitrogen in ammonium and nitrate, carbon dioxide, and O2.