Information technologies have been pervasively applied by NIMML, ranging from lab management systems, data warehousing and digital libraries, data analysis and visualization tools, web technologies, to in silico experimental platforms and high performance computing (HPC).

• Lab management systems: NIMML has a wide range of lab management needs, for example, lab inventory management, mouse colony management, clinical trial and pre-clinical experiment management. With software management systems, bookkeeping costs are greatly reduced and efficiency and accuracy have been significantly improved.
• Data warehousing and digital libraries: NIMML is a high throughput lab with a large volume of experimental data generated daily. The data are with different formats and are from different assays. In addition to the data themselves, all the associated settings and configurations are required to be stored so that the data can be appropriately interpreted. We use databases to manage these extremely challenging and complex biological experimental data. On the top of the databases, digital libraries technologies are provided to search and query the data meaningfully.
• Data analysis and visualization tools: NIMML has adopted data analysis and visualization tools used for data from flow cytometry, gene sequencing, histology, and proteomics, transcriptomics, and lipidomics. Some of the tools are developed in house by our informatics team as fully pipelined open source tools.
• Web technologies: most of NIMML management systems and data analysis tools have web interfaces providing high availability and high usability. Users get consistent experiences across different systems and tools. Our lab website also serves as a portal to disseminate NIMML’s efforts to the broader research communities.
• In silico experimental platforms and high performance computing (HPC) are of special strength in NIMML. The Modeling Immunity to Enteric Pathogens (MIEP) program is funded through NIAID and focuses on modeling and simulating the gut immune responses to enteric pathogens. Through this program, we have build up powerful HPC computational infrastructural platforms and these HPC resources can furthermore support large scale in silico experimental needs and can significantly reduce the high costs associated with biological and animal experiments.