Biological regulatory network inference
 Richard Bonneau, NYU

We have recently described a network inference algorithm, the Inferelator,
which infers regulatory influences for genes and/or gene clusters from mRNA
and/or protein expression levels. The procedure can simultaneously model
equilibrium and time-course expression levels, such that both kinetic and
equilibrium expression levels may be predicted by the resulting models.
Through the explicit inclusion of time, and gene-knockout information, the
method is capable of learning causal relationships. It also includes a novel
solution to the problem of encoding interactions between predictors. We
discuss the results from an initial application of this method to the
halophilic archaeon, Halobacterium NRC-1. We have found the network to be
predictive of 130 newly collected microarray datasets and have also validated
parts of the network using ChIP-chip. 

I'll go over the algorithm, the current bottlenecks and some new directions we
hope to take to improve the stability of our networks over longer time scales.
I'll also discuss methods for scaling up the work to multiple-species
data-sets and multi-cellular organisms.

  http://genomebiology.com/2006/7/5/R36

  http://www.biomedcentral.com/1471-2105/7/280

  http://www.nature.com/msb/journal/v3/n1/full/msb4100118.html