A tool for ODE-based modeling of dynamic gene regulatory networks with heuristic search of the topology network structure minimizing both the model fit error and the number of edges that are not supported by prior knowledge. NetGenerator is used for small-scale networks (<30 nodes).
The algorithm has been implemented as a package in the programming language / statistical computing environment R. It is available in form of a testing bundle containing both the algorithm and the examples at http://www.biocontrol-jena.com/NetGenerator/NetGeneratorBundle.zip
- Guthke R, Möller U, Hoffmann M, Thies F, Töpfer S. Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection. Bioinformatics. 2005;21(8):1626-34.[Link]
- Weber M, Henkel SG, Vlaic S, Guthke R, van Zoelen EJ, Driesch D. Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0. BMC Syst Biol. 2013;7:1. [Link]
- Schulze S, Henkel SG, Driesch D, Guthke R, Linde J (2015) Computational prediction of molecular pathogen-host interactions based on dual transcriptome data Front Microbiol 2015;6:65. [Link]
TILAR – Network Reconstruction
In this tutorial we will infer a gene regulatory network (GRN) from human gene expression data by integrating transcription factor binding site (TFBS) predictions...
ExTILAR – Network Inference
ExTILAR extends the TILAR algorithm by adding a variety of new aspects to the modeling concept to support time resolved data. In this tutorial we will show how to infer a dynamic transcription factor network using time series data of cultivated murine hepatocytes.