Title: Quantitative studies of microRNA-mediated regulation
Description: This dissertation has three parts and primarily concerns microRNAs (miRNA), a recently discovered class of regulatory molecules in plants and animals. While hundreds of miRNAs have been identified in animals, the function of most are unknown. By examining large-scale gene expression data and the evolution of putative miRNA binding sites, the first part of this dissertation describes computational methods and analyses to infer whether certain miRNA-containing circuit topologies are prevalent in mammals (Chapter 2). I found that the expression of most miRNAs I analyzed is positively and/or negatively correlated with that of their targets, suggesting that these miRNAs are likely mediating a large number of incoherent and/or coherent feedforward and/or feedback circuits. The functional implications of these findings are discussed. The second part of this dissertation presents a novel computational framework to infer individual miRNA functions by assessing whether a miRNA functionally targets genes in a gene set, such as genes in a network (Chapters 3 and 4). Functional association between a miRNA and a gene set is inferred based on determining whether the putative binding sites in the gene set are evolving at a slower rate or have higher site-quality scores than random gene sets. A novel iterative nearest-neighbor procedure to generate random gene sets with properties comparable to the input gene set is introduced to correct for systematic biases. I apply this method to a large number of gene sets to predict human miRNA functions and gene-set-specific miRNA targets. The biology of some top predictions is discussed, many of which fit well with existing experiments and form a basis for future experimental studies of human miRNAs.

In the last part of this dissertation I develop mathematical models of miRNA-mediated repression (Chapter 5). Despite rapid research progress, a quantitative understanding of miRNA-mediated repression is lacking and the underlying repression mechanism(s) are under intense debate. Here I first derive a steady-state model relating miRNA and target-mRNA levels to target-protein levels based on existing experiments. This model has two robust qualitative predictions. First, miRNAs can precisely define target-mRNA-level thresholds below which translation is kept at a basal rate. Second, miRNAs can fine-tune target protein expression where the dynamic range largely depends on the number of binding sites. The evolutionary implications of these predictions are discussed. Next I model several plausible mechanisms of miRNA-mediated repression to explore whether existing experimental data can be used to constrain the miRNA-mediated repression mechanism. I show that a class of models, including the ribosome drop-off model but not the translation-initiation model, can give rise to an exponential (i.e. highly non-linear) relationship between the saturated repression levels and binding-site numbers, fitting well with existing experiments. Future experiments are proposed to validate key predictions of the models.