Integrative approaches for systematic reconstruction of regulatory circuits in mammals

Abstract

The reconstruction of regulatory networks is one of the most challenging tasks in systems biology. Although some models for inferring regulatory networks can make useful predictions about the wiring and mechanisms of molecular interactions, these approaches are still limited and there is a strong need to develop increasingly universal and accurate approaches for network reconstruction. This problem is particularly challenging in mammals, due to the higher complexity of mammalian regulatory networks and limitations in experimental manipulation. In this thesis, I present three systematic approachs to reconstruct, analyse and refine models of gene regulation. In Chapter 1, I devise a method for deriving an observational model from temporal genomic profiles. I use it to choose targets for perturbation experiments in order to determine a network controlling the responses of mouse primary dendritic cells to stimulation with pathogen components. In Chapter 2, I introduce the algorithm Exigo, for identifying essential interactions in regulatory networks reconstructed from experimental data where regulators have been silenced, using a network reduction strategy. Exigo outperforms previous approaches on simulated data, uncovers the core network structure when applied to real networks derived from perturbation studies in mammals, and improves the performance of network inference methods. Lastly, I introduce in Chapter 3 an approach to learn a module network from multiple highthroughput assays. Analysis of a diffuse large B-cell lymphoma dataset identifies candidate regulator genes, microRNAs and copy number aberrations with biological, and possibly therapeutic, importance.