German scientists are unravelling the genetic basis of diseases by combining computational biology techniques.

Silke Sperling of the Max Planck Institute for Molecular Genetics, Berlin, and colleagues have used various data analysis methods to study gene expression. By looking at how gene expression levels vary between heart patients with different symptom patterns they were able to identify genes that may be involved in certain heart conditions. 'We are very excited,' says an enthusiastic Sperling. 'It is nice to puzzle things together and see meaningful data come out.'

Data analysis leads to networks showing relationships between gene expression and characteristics related to heart disease

With their data the researchers drew up networks showing how the products of some genes regulated the expression of others. The method can predict interactions between transcription factors - proteins that regulate gene expression - and their targets, which is a key feature, explains Sperling.

Sperling's research confirmed earlier results achieved by in vitro biochemical methods, proving the reliability of the approach. But not only that, the group also found new interactions between transcription factors and targets, showing that the method can be used to explore the genetic processes underlying disease. 

The researchers say that the key behind their approach's success is in how the computational techniques are combined, producing more significant and reliable results than using individual methods alone.

The research is at the centre of Sperling's interest in the genetics of heart disease, but she says it could benefit studies into any medical condition. The method is also capable of handling large amounts of data, Sperling adds. 'It was important to prove the method first. Next we will try to go to the genome-wide level.'

Danièle Gibney