3 research outputs found
Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli
The set of regulatory interactions between genes, mediated by transcription
factors, forms a species' transcriptional regulatory network (TRN). By
comparing this network with measured gene expression data one can identify
functional properties of the TRN and gain general insight into transcriptional
control. We define the subnet of a node as the subgraph consisting of all nodes
topologically downstream of the node, including itself. Using a large set of
microarray expression data of the bacterium Escherichia coli, we find that the
gene expression in different subnets exhibits a structured pattern in response
to environmental changes and genotypic mutation. Subnets with less changes in
their expression pattern have a higher fraction of feed-forward loop motifs and
a lower fraction of small RNA targets within them. Our study implies that the
TRN consists of several scales of regulatory organization: 1) subnets with more
varying gene expression controlled by both transcription factors and
post-transcriptional RNA regulation, and 2) subnets with less varying gene
expression having more feed-forward loops and less post-transcriptional RNA
regulation.Comment: 14 pages, 8 figures, to be published in PLoS Computational Biolog
Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms
Motivation :Reconstructing the topology of a gene regulatory network is one
of the key tasks in systems biology. Despite of the wide variety of proposed
methods, very little work has been dedicated to the assessment of their
stability properties. Here we present a methodical comparison of the
performance of a novel method (RegnANN) for gene network inference based on
multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER),
focussing our analysis on the prediction variability induced by both the
network intrinsic structure and the available data.
Results: The extensive evaluation on both synthetic data and a selection of
gene modules of "Escherichia coli" indicates that all the algorithms suffer of
instability and variability issues with regards to the reconstruction of the
topology of the network. This instability makes objectively very hard the task
of establishing which method performs best. Nevertheless, RegnANN shows MCC
scores that compare very favorably with all the other inference methods tested.
Availability: The software for the RegnANN inference algorithm is distributed
under GPL3 and it is available at the corresponding author home page
(http://mpba.fbk.eu/grimaldi/regnann-supmat