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TITLE: ELUCIDATION OF A GENE REGULATORY NETWORK FOR FOREBRAIN DEVELOPMENT
USING BIOINFORMATICS APPROACHES FOR THE ANALYSIS OF COMPILED MICROARRAY
DATASETS
AUTHORS (ALL): Gohlke, Julia M.1; Parham, Frederick M.1; Parker, Joel2; Smith, Marjolein
V.2; Portier, Christopher J.1.
SPONSOR NAME: None
INSTITUTIONS (ALL): 1. Laboratory of Molecular Toxicology, NIEHS, RTP, NC, USA.
2. Constella Health Sciences, Durham, NC, USA.
ABSTRACT BODY: Computational models can facilitate elucidation of the multitude of
dynamic gene and protein interactions that govern the mechanism of a toxicological
response. In the field of systems biology, there has been considerable discussion of
“algorithm-based” versus “literature-based” approaches. In particular, algorithm-based
approaches have been criticized for utilizing data generated solely by novel high-throughput
techniques at the gene, protein, or metabolite level, while excluding data generated using
more traditional approaches at various levels of organization. Alternatively, literature-based
approaches attempt to incorporate data from numerous sources and at various levels, but
they may miss important novel discoveries gleaned from the integration of broad gene or
protein expression scans. Here we directly compare algorithm-based to literature-based
methodologies for elucidation of gene regulatory networks (GRN). First, we build a GRN from
compilation of the current literature on regulation of mammalian forebrain development using
diverse experimental approaches. We then quantify this literature-based GRN (L-GRN) using
microarray datasets from several transgenics and gain-of-function perturbations ( Ngn1-/-,
Ngn2-/-, Mash1-/-, Ngn1/Ngn2-/-, Ngn2/Mash1-/-, Dlx1/2-/-, Ngn2GOF and Mash1GOF)
using Bayesian statistical approaches. An alternative GRN is generated using a Bayesianbased algorithm (A-GRN) utilizing only the microarray dataset. We are able to show that both
methodologies have distinct benefits, namely hypothesis testing in the quantification of the LGRN and hypothesis generation in the A-GRN. For example, Gsh1 regulation of Dlx 1 and Dlx
2 during the differentiation of GABAergic neurons in the ventral forebrain is not supported by
the micorarray dataset, whereas a novel linkage between Pax6 and Nscl1 during the
differentiation of glutamatergic neurons in the dorsal forebrain is suggested by the analysis.