Understanding the nature of interpopulation interactions in hostassociated microbial communities is critical to understanding gut colonization, responses to perturbations, and transitions between health and disease. Characterizing these interactions is complicated by the complexity of these communities and the observation that even if populations can be cultured, their in vitro and in vivo phenotypes differ significantly. Dynamic models are the cornerstone of computational systems biology and a key objective of computational systems biologists is the reconstruction of biological networks (i.e., network inference) from high-throughput data. When such computational models reflect biology, they provide an opportunity to generate testable hypotheses as well as to perform experiments that are impractical or not feasible in vivo or in vitro. We modeled time-series data for murine microbial communities using statistical approaches and systems of ordinary differential equations. To obtain the dense time-series data, we sequenced the 16S ribosomal RNA (rRNA) gene from DNA isolated from the fecal material of germfree mice colonized with cecal contents of conventionally raised animals. The modeling results suggested a lack of mutualistic interactions within the community. Among the members of the Bacteroidetes, there was evidence for closely related pairs of populations to exhibit parasitic interactions. Among the Firmicutes, the interactions were all competitive. These results suggest future animal and in silico experiments. Our modeling approach can be applied to other systems to provide a greater understanding of the dynamics of communities associated with health and disease.microbiome | microbial ecology | culture independent | 454 sequencing | dynamical systems A nalysis of microbial communities is complicated by the communities' large size, diversity, and recalcitrance to culturing (1). Furthermore, even if a population can be cultured and studied under in vitro conditions, there is no guarantee that the observed phenotypes replicate an in vivo phenotype. An underused strategy for describing in vivo phenotypes is the use of quantitative models to describe temporal patterns of biodiversity. Generation of mathematical models can facilitate the inference of relative growth rates, mechanisms of interaction, and responses to perturbations (2-5).Mathematical models are powerful because they provide a method to explain past experiments and predict the outcomes of future experiments. A commonly used approach in microbial ecology literature is to develop correlation-based networks to describe the co-occurrence and dynamics of populations (6-8). These models are helpful for providing an initial description of the interaction network; however, they ignore the possibility that a relationship can be asymmetrical with one partner benefiting and the other being hindered. Furthermore, application of these methods to time-series data violates assumptions of independence between observations (9). A second approach that has received at...