Identification of genetic network dynamics with unate structure

Modern experimental techniques for time-course
measurement of gene expression enable the identification of
dynamical models of genetic regulatory networks. In general,
identification involves fitting appropriate network structures and
parameters to the data. For a given set of genes, exploring all possible
network structures is clearly prohibitive. Modelling and identification
methods for the a priori selection of network structures compatible
with biological knowledge and experimental data are necessary to
make the identification problem tractable.
We propose a differential equation modelling framework
where the regulatory interactions among genes are expressed in
terms of unate functions, a class of gene activation rules commonly
encountered in Boolean network modelling. We establish analytical
properties of the models in the class and exploit them to devise
a two-step procedure for gene network reconstruction from product
concentration and synthesis rate time series. The first step isolates
a family of model structures compatible with the data from a set of
most relevant biological hypotheses. The second step explores this
family and returns a pool of best fitting models along with estimates
of their parameters. The method is tested on a simulated network
and compared to state-of-the-art network inference methods on the
benchmark synthetic network IRMA.