MCA: A Computational Framework to Study microRNAs in Cell-Cell Interactions

Cell to cell Interactions are essential for the overall function of multicellular organisms. Recent studies have discovered that non-coding microRNAs (miRNAs) can be transferred from one cell to another cell to perform certain physiological functions. Understanding which miRNAs participate in this intercellular crosstalk and how they function under specific environmental conditions is essential to gaining a new perspective of intercellular communications.

Biotechnology advancement has generated a large amount of transcriptomics data that motivate efficient computational strategies to understand miRNAs in cell-cell interactions, which is fundamental to modeling biological systems. This project investigation miRNAs in cell-cell interactions will generate methods and tools that will be freely available to the scientific community.

The research will be incorporated into graduate, undergraduate and K-12 education, and will be disseminated to the research community and the public to enhance scientific understanding.

The project seeks to integrate large-scale transcriptomics data and create efficient computational algorithms to model miRNA activities relevant to cell-cell interactions under different conditions. The advanced statistical algorithms and probabilistic models of miRNA activities related to cell-cell interactions promise to unveil various roles of miRNAs in cell communications and phenotype formulation.

The research is expected to not only advance scientific understanding of cell communications but also stimulate interest in developing and advancing efficient computational modeling and data integration methods in the informatics research field. Outputs of the project will be made available through the project website (http://hulab.ucf.edu/research/projects/cell2cell/).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.