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.
Coarse-Grained Reverse Engineering of Genetic Regulatory Networks