The complex and dynamics of microbial community are major factors in ecology system. With NGS technique, metagenomics data provides a new source to explore microbial interactions. Lotka-Volterra models have been widely used to infer animal interaction of dynamic systems and recently been applied to analyze metagenomic data. In this paper, we presented the first Lotka-Volterra model based tool, Metagenomic Microbial Interaction Simulator (MetaMIS), to analyze time series data of microbial community profiles. MetaMIS firstly infers the underlying microbial interactions from operational taxonomic units (OTU) abundance tables and interprets interaction models by the use of Lotka-Volterra model. We also embedded Bray-Curtis dissimilarity method in MetaMIS to evaluate the resemblance of biological reality. MetaMIS was designed to tolerate a high level of missing data, it can estimate the interaction information without the influence from rare microbes. For each interaction model, MetaMIS systematically examines interaction patterns (such as mutualism (+/+), competition (-/-), parasitism or predation (+/-), commensalism (+/0), amensalism (-/0), and no effect (0/0)) and refines the biotic role inside microbes. The output of MetaMIS can be exported as Gephi or Cytoscape format for advanced analysis. In a test case, we collected the human female gut microbiota which contained 124 time points of 88 OTUs at the family level. Through the test, MetaMIS generated 55 interactions in around 5 minutes on a standard desktop computer, the results also revealed that rare species may play important roles in the microbial dynamic system. MetaMIS provides an efficiency and user-friendly platform and may reveal new insights from metagenomics data.