Network inference and analysis for understanding and predicting protein function

Project leader

Funding source

Swedish Research Council - Vetenskapsrådet (VR)

Project Details

Start date: 01/01/2016
End date: 31/12/2019
Funding: 3550000 SEK


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 plan to incorporate new data and data types, and to develop new algorithms to handle them (aim 1). We further plan to develop network analysis algorithms that exploit FunCoup for identification of candidate disease genes and pathway annotation of experimental gene sets (aim 2). Finally, to understand regulatory mechanisms we are complementing FunCoup's global association networks with perturbation-induced modelling of dynamical gene regulatory networks (GRN), for which the FunCoup network serves as both a hypothesis generator of the subnetwork, and as a prior during GRN inference (aim 3). The project will make important contributions to understanding the functional interactome on a global scale, with detailed mechanistic insights of subnetworks.

Last updated on 2017-28-07 at 11:18