Abstract of John Hertz

John Hertz (Nordita) and Mattias Wahde (Chalmers University)

We have modeled genetic regulatory networks in the framework of continuous-time recurrent networks. We determine the network parameters from gene expression level time series data using neural network learning and genetic algorithms. We have applied the method to artificial data and to expression data from the development of rat central nervous system, where the active genes cluster into four groups, within which the temporal expression patterns are similar. The data permit us to identify approximately the interactions between these groups of genes. We find that generally a single time series is of limited value in determining the interactions in the network, but multiple time series collected under different but similar conditions (e.g. in related tissues or under treatment with different drugs) can fix their values much more precisely. References:

Coarse-Grained Reverse Engineering of Genetic Regulatory Networks