A cell-based probabilistic approach unveils the concerted action of miRNAs

Shelly Mahlab-Aviv, Nathan Linial, Michal Linial*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Mature microRNAs (miRNAs) regulate most human genes through direct base-pairing with mRNAs. We investigate the underlying principles of miRNA regulation in living cells. To this end, we overexpressed miRNAs in different cell types and measured the mRNA decay rate under a paradigm of a transcriptional arrest. Based on an exhaustive matrix of mRNA-miRNA binding probabilities, and parameters extracted from our experiments, we developed a computational framework that captures the cooperative action of miRNAs in living cells. The framework, called COMICS, simulates the stochastic binding events between miRNAs and mRNAs in cells. The input of COMICS is cell-specific profiles of mRNAs and miRNAs, and the outcome is the retention level of each mRNA at the end of 100,000 iterations. The results of COMICS from thousands of miRNA manipulations reveal gene sets that exhibit coordinated behavior with respect to all miRNAs (total of 248 families). We identified a small set of genes that are highly responsive to changes in the expression of almost any of the miRNAs. In contrast, about 20% of the tested genes remain insensitive to a broad range of miRNA manipulations. The set of insensitive genes is strongly enriched with genes that belong to the translation machinery. These trends are shared by different cell types. We conclude that the stochastic nature of miRNAs reveals unexpected robustness of gene expression in living cells. By applying a systematic probabilistic approach some key design principles of cell states are revealed, emphasizing in particular, the immunity of the translational machinery vis-a-vis miRNA manipulations across cell types. We propose COMICS as a valuable platform for assessing the outcome of miRNA regulation of cells in health and disease.

Original languageAmerican English
Article numbere1007204
JournalPLoS Computational Biology
Issue number12
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 Mahlab-Aviv et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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