### Reconstruction of Genetic Networks from Gene Expression Perturbation Data Using a Boolean Model

Ronald Taylor^{1}

^{1}ronald.taylor@uchsc.edu, U of Colorado

This work explores the use of Boolean models in reconstruction of the
topology of genetic transcriptional networks. The construction and
employment of a software suite for such exploration is described. The
program suite forms a testbed for reconstructions of the regulatory
edges of simulated networks of different types, using a Boolean model
for the gene expression values and the node states in the networks.
Using gene expression data from simulated perturbations, the relative
difficulty of reconstruction of different networks is
measured. Important network parameters are determined. Target
in-degree is found to be the most important variable. Also, the
effects of noise (random errors) in the gene expression measurements
are described. Also, different inference methods are compared against
the same networks, for measurement of their relative power. The value
of control points into the networks (settable inputs into the nodes)
is described. The testbed is used to refine one of the original
inference methods, conditional mutual information inference (CMI),
doubling its power in terms of the target in-degree it can handle.