Learning global transcriptional dynamics with the Inferelator and cMonkey

Richard Bonneau
New York University

Our system for network inference and modeling consists of two major components: cMonkey and the Inferelator. We describe their integration into a functioning integrated system applied to several prokaryotic organisms.
cMonkey groups genes and conditions into biclusters on the basis of 1) coherence in expression data across subsets of experimental conditions, 2) co-occurrence of putative cis-acting regulatory motifs in the regulatory regions of bicluster members and 3) the presence of highly connected sub-graphs in metabolic and functional association networks. We describe the algorithm and the results of extensive tests of several previously described methods, showing that cMonkey has several advantages in the context of regulatory network inference.
The Inferelator is a network inference algorithm that 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.
For Background on our integrated system see:




Back to Workshop IV: Search and Knowledge Building for Biological Datasets