Inferring Genetic Networks from Gene Expression Data

Standard methods for the analysis of microarray data are often emphasizing on the identification of single genes within the process of interest only and thus neglecting important information like the time dependencies hidden in the data sets. From a systems biology point of view it is therefore necessary to develop a new class of analytical methods. One major aspect that has to be addressed by these new methods is to understand the regulatory mechanisms within a cell.

National Genome Research Project

The NGFN2 explorative project "Inferring Genetic Networks from Gene Expression Data" aims to analyse genomic data with sophisticated approaches. It is divided into the following three major subprojects:

Functional Clustering

To reduce the size of the data sets, the first step is to filter genes that did not appear to participate in the biological process of interest. We have developed intelligent clustering techniques that incorporate biological and especially functional information based on Gene Ontology (GO).

Clustering Functional Clustering based on GO

Contact: Nora Speer

Modeling and Simulation

For the inference process, mathematical models are used to understand the dependencies within a genome. Mathematical modeling provides a powerful approach to abstract the high complexities of a biological system. Over the last year, we developed a software framework that aims to infer gene networks from microarray data and also metabolic systems from experimental data.

Modeling JCell - Java based framework for inference of genetic networks

Contact: Christian Spieth

Data Integration

Main goal is to construct models of gene regulatory networks by analyzing and integrating signals from various experiments. We plan to combine hypothesis-driven methods with data-driven research. As groundwork we build biologically motivated models which are intuitive and capture a high level of detail. The hypothesis-driven modeling effort will form a basis for our data-driven research. Thereby we will integrate heterogeneous experimental observations with biologically motivated models.

Integration Modeling of gene expression

Contact: Jochen Supper

This project is funded as an explorative project by:

NGFN bmb+f