Systems Biology approach to identify key players from microarray time series

Modern high-throughput methods allow the simultaneous measurement of
many cell parameters. However, it is still a question of debate how to
extract individual, yet important gene and/or protein interactions
from this large-scale, high-dimensional data. Therefore, the
identification of key players from genome-wide transcriptome profiling
experiment should be of general interest.

To this end, we mapped artifical gene regulatory network simulations
taken from the DREAM challenge onto a 2 dimensional space using
multi-dimensional scaling (MDS). We found that the low-dimensional
data reconstruction strongly reflected the network topology and
allowed for identification of input/output nodes of the networks.

In biological systems, input/output nodes of a cellular network should
have a high impact on the development of a phenotype. To test this
hypothesis we applied our approach to microarray time series data on
HGF-induced keratinocyte migration. Genes were assigned a relative
impact score on migration according to their spatial location after
the MDS mapping. As a result we found few genes having a high score,
thus acting as putative key players in the decision towards migration.
 Based on literature data as well as our own experiments we
demonstrate a decrease in migratory activity when inhibiting several
of those key regulator gene products, well in line with our analysis.
Therefore, we conclude that this approach might be applicable to
microarray time series in general, leading to rapid key player
identification from high-throughput data.