Publication
The expression of genes is controlled by regulatory networks, which performspecific functions in a cell. Gene networks play a crucial role in the development of multicellular organisms by precisely coordinating spatial and temporal gene expression patterns during different developmental stages. Unravelling and modelling these networks is of key importance to gain eventually a complete understanding of developmental processes and genetically related diseases. In this thesis, we present a comprehensive framework for reverse engineering gene regulatory networks, which required the development of many methods in very diverse research fields. A second important contribution is their implementation as extensible, userfriendly and open source computational toolsa. Over the last decade, numerous methods have been developed for inference of regulatory networks fromgene expression data. However, relatively little effort has been put into evaluating the performance of those methods due to the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNWprovides a networkmotif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic (ROC) curves. Furthermore, we used GNW to provide the international DREAM (Dialogue for Reverse Engineering Assessments andMethods) competition with three network inference challenges (DREAM3, DREAM4, and DREAM5). In the context of the DREAM competition, 91 teams submitted about 900 network predictions to evaluate the performance of their methods on GNW-generated benchmarks. Today, the accuracy of more than 25,000 gene network reconstructions have been evaluated by GNWusers. Gene regulatory networks are often organized into groups, modules or community of related genes and proteins carrying out specific biological functions. Here, we also address the rational decomposition of (reconstructed) biological networks into function modules. We presentan extensible and modular framework for community structure detection in networks called Jmod. Jmod implements state-of-the-art community structure detection methods including Newman’s spectral algorithm and a genetic algorithm-basedmodularity optimization method that we developed. The performance of these methods has been evaluated on biological and in silico networks. The application of thesemethods is actually not limited to gene regulatory networks as they can also provide ins