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microRNAs emerged in the past decade as important regulators of geneexpression, essential for many processes including embryonicdevelopment, cell differentiation, metabolism and immunity. Theyappear able to drive, on their own, the induction of pluripotency insomatic cells. Although initially thought to induce translationinhibition without affecting mRNA levels, it has become clear miRNAsdo induce mRNA degradation, with some delay relative to translationalinhibition. Surprisingly, transfection or induction of miRNAexpression appear to have small effects on the targetmRNAs. Therefore, one of the open questions in the field is how thedramatic phenotypic effects of miRNAs are brought about by relativelysmall changes in target mRNA levels. Various hypotheses have beenproposed to explain this paradox, one being that miRNAs reduce thevariance rather than the average levels of their targets, specificallythrough regulatory network motifs that they form with transcriptionfactors. Such motifs involve a transcription factor (TF) thatactivates the expression of a miRNA and of an mRNA target, the miRNApost-transcriptionally down-regulating the same target. Thisfeed-forward loop (FFL) is called 'incoherent' (iFFL) because the TFand miRNA have opposite effects on the common target, the ultimateoutcome being a reduction in the fluctuations that the common targetexperiences. Especially through computational analysis, a number ofexamples of iFFLs have been discovered.In the past years we developed methods for identification of miRNAtargets and for estimating their effects. Specifically, we obtainedbinding sites of the Argonaute protein through crosslinking andimmunoprecipitation and used them to infer a biophysical model ofmiRNA-target interaction. We showed that this model enables us toaccurately identify targets of individual miRNAs, including those thatdo not perfectly match the 5' end of the miRNA (also callednon-canonical). We further collaborated with the group of Erik vanNimwegen on modeling mRNA levels in terms of transcriptionalregulation by transcription factors and post-transcriptionalregulation by miRNAs. The model that we developed is able to identifykey miRNA and TF regulators in individual cell types (embryonic stemcells) or processes (epithelial-to-mesenchymal transition, EMT). Wetherefore would like to develop this model further in the coming yearsby1. Comprehensively identifying promoters of miRNA genes and predictingthe associated transcription regulatory elements.2. Extending the set of curated transcription factor regulatory motifsbased on recently released chromatin immunoprecipitation data.3. Analyzing a fine-grained time course of miRNA and mRNA expressionduring EMT (that will be obtained in our lab) in the context of acomputational model, to identify and characterize the dynamics ofnetworks involving miRNAs and transcription factors that operateduring EMT.4. Extending the model to the prediction of key splicing regulatoryfactors, some of which are known to be targeted by miRNAs and/or playimportant roles in EMT.I believe that this project will result in new predictive tools thatcould be generally used to identify key regulators of gene expressionin specific cell types and at the transition between celltypes. Furthermore, by applying our computational tools to EMT we willon the one hand have the possibility to validate the model against thelarge body of available data and on the other hand to contribute newinsights to this process of high medical relevance.