Reconstruction of Biological Networks

Reconstruction of biological networks from high-throughput data is one of the main challenges of computational biology. The understanding of the interconnection between the elements of the genetic, metabolic or signalling networks, gives insight into the inner mechanisms of the cell and paves the way towards various applications in pharmacology.

The reconstruction techniques are usually based on perturbation experiments, e.g., gene knockouts or knockdowns, in which one or more network nodes (e.g. genes) are systematically perturbed and the effects on the other nodes are observed. More concretely, in the context of genetic regulatory networks with knockout experiments, the nodes of the networks are genes. Each gene is knocked out at least once and the expressions of the other genes are measured for each knockout experiment. The expression change with regard to the unperturbed wild type defines the influence of the knocked out gene on the other genes. Based on that, connections between the genes can be established. Using the difference in the expression between the perturbed and the wild type, weights and signs can be associated with the connections to quantify the influence and indicate over- and under-expression, respectively.

We develop scalable and efficient reconstruction methods. By exploiting the strength of new software and hardware technologies, e.g., GPUs, we are able to cope with genome size data