PRiME is an NIAID funded project. Its main goals are to develop data-based models for the response of human dendritic cell to pathogenic viral infection, and to provide education in computational immunology to researchers from various research fields.
The project encompasses several research centers working in collaboration. In the interdisciplinary center at Mount Sinai School of Medicine we have been actively mapping the regulatory network that is activated by viral infection in dendritic cells by analyzing experimental data. Using this network, we developed models and performed simulations to further our understanding of the innate immune response. Read more.
New and Updated!
A network model of protein-protein interactions in human dendritic cells using a Bayesian integration framework. The network integrates PRIME-generated data with other publicly available datasets.
A fast algorithm to cluster flow cytometry data using the peaks of the density function.
A method to infer transcriptional networks that underlie dynamic cellular responses, obtained from time-series of gene expression data using statistically rigorous enrichment analysis.
A web-tool to allow a round trip of data between DBs and data analysis tools.
A web accessible tool for inferring functional signaling networks from early gene expression data.
A web-based tool for biological pathway publishing that facilitates interactive curation and sharing of pathway knowledge-bases.
A system that integrates microarray data from PRiME experiments, as well as several publicly accessible datasets investigating the anti-viral response in dendritic cells.
A web-based tool to analyze and visualize correlations within gene expression data, overlaid with annotations resulting from transcription factor enrichment analysis.
A new compensation method for flow cytometry data based on multi-variable optimization.
An electronic lab notebook system.
DC Signaling Pathway
Signaling network of the dendritic cell in response to pathogens: a knowledgebase that is supported by the research community input.
A fast unsupervised clustering algorithm designed for flow cytometry gating.
QuSAGE R package
QuSAGE can be used to quantify and statistically compare pathway activities between two responses.