Modeling genetic regulatory dynamics in neural development

We model genetic regulatory networks in the framework of continuous-ti-me r ecurrent networks. The network parameters are determined from gene expressi on level time series data using genetic algorithms. We have applied the met hod 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 approximate ly 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 in related tissues or under treatment with different drugs can fix their values much more precisely.