The cell is a complex network of interconnected entities, whose activity is regulated by a diverse range of mechanisms and is modified by multiple internal and external perturbations. This talk describes computational methods that utilize high-throughput data and statistical models to reconstruct these networks and understand how their activity is modified by various factors. I will describe a model for transcriptional gene regulation and a computational method for learning this model from gene expression data. I will show how gene expression data from a population of genetically diverse individuals can be used to uncover genetic mechanisms that cause phenotypic diversity, and how gene expression from diverse immune system cells can be used to uncover the mechanisms that regulate hematopoietic cell differentiation. In the second part of the talk, I will also describe a new approach for analyzing next-gen sequencing ribosomal footprinting data. The results of this analysis can be used to help identify factors involved in the key steps of translation: initiation and elongation.