Working Together: Combinatorial Regulation by microRNAs

  • Yitzhak Friedman
  • Ohad Balaga
  • Michal Linial
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 774)


MicroRNAs (miRNAs) negatively regulate gene expression level of mRNA post-transcriptionally. Deep sequencing and large-scale screening methods have yielded about 1,500 miRNA sequences in human. Each miRNA contains a seed sequence that is required, but not sufficient, for the correct matching with its targets. Recent technological advances make it possible to capture the miRNAs with their cognate mRNAs at the RISC complex. These experiments have revealed thousands of validated mRNA-miRNA pairing events. In the context of human stem cells, 90% of the identified transcripts appear to be paired with at least two different miRNAs.

In this chapter, we present a comprehensive outline for a combinatorial regulation mode by miRNAs. Initially, we summarize the computational and experimental evidence that support a combined effect of multiple miRNAs. Then, we describe miRror2.0, a platform specifically convened to consider the likelihood of miRNAs cooperativity in view of the targets, tissues and cell lines. We show that results from miRror2.0 can be further refined by an iterative procedure, calls Psi-miRror that gauges the robustness of the regulation. We illustrate the combinatorial regulation projected onto graphs of human pathways and show that these pathways are amenable to disruption by a small set of miRNAs. Finally, we propose that miRNA combinatorial regulation is an attractive regulatory strategy not only at the level of single target, but also at the level of pathways and cellular homeostasis. The joint operation of miRNAs is a powerful means to overcome the low specificity inherent in each individual miRNA.


microRNA Database Prediction tools • 3′-UTR Genomics Deep sequencing Regulatory pathway Bioinformatics 







disconnecting score


gene ontology


high-throughput sequencing of RNAs isolated by cross-linking immunoprecipitation


photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation

miRNA (miR)



machine learning


mass spectrometry


non-coding RNA RISCRNA-induced silencing complex


stable isotope labeling by amino acids in cell culture


untranslated region.



We thank Guy Naamati whose contribution to the miRror platform development was seminal. We thank Nati Linial for insightful ideas. We thank Solange Karsenty for maintenance of the miRror website. We thank Manor Askenazi for a critical reading and editing of the manuscript. A student fellowship (O.B) is awarded by the SCCB, the Sudarsky Center for Computational Biology. We apologize to those that we could not cite. In numerous instances we replace the primary citations by review articles due to space constraints. This study is partially supported by the ISF 592/07, the BSF 2007219 and the EU Framework VII of Prospects.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Biological Chemistry, Institute of Life SciencesThe Hebrew University of JerusalemJerusalemIsrael
  2. 2.School of Computer Science and EngineeringThe Hebrew University of JerusalemJerusalemIsrael

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