Research project Network approaches for inferring gene function and regulation
High-throughput biology (genomics, proteomics, transcriptomics, metabolomics, etc.) is producing massive amounts of biological data that in different ways can help us understand biology. A major challenge is to turn this Big Data into knowledge that generates novel biological insights.
The goal of this project is to discover protein function from gene/protein network analysis, with a focus on disease pathways. By building an integrated high-quality map of the “functional coupling interactome” we create a comprehensive network resource called FunCoup that serves as the foundation for our network analyses, as well as a resource for the scientific community.
To keep FunCoup state-of-the-art we will incorporate new data and data types, and develop new algorithms to use data (aim 1). To improve the use of FunCoup we will develop new network analysis algorithms that exploit networks for pathway annotation of experimental gene sets and identification of candidate disease genes (aim 2). To complement FunCoup´s global association networks with directed causal regulatory influences, we will develop algorithms for perturbation-based inference of gene regulatory networks (GRNs) (aim 3). The focus is on algorithms to infer reliable GRNs from real data, assess their predictiveness, and to scale up the methods to larger systems.
The project will make important contributions to understanding the functional interactome on a global scale, with detailed mechanistic insights of subnetworks.
Project members
Project managers
Erik Sonnhammer
Professor of Bioinformatics
