Least absolute regression network analysis of the murine osteoblast differentiation network.
BACKGROUND
We propose a reverse engineering scheme to discover genetic regulation from genome-wide transcription data that monitors the dynamic transcriptional response after a change in cellular environment. The interaction network is estimated by solving a linear model using simultaneous shrinking of the least absolute weights and the prediction error.


RESULTS
The proposed scheme has been applied to the murine C2C12 cell-line stimulated to undergo osteoblast differentiation. Results show that our method discovers genetic interactions that display significant enrichment of co-citation in literature. More detailed study showed that the inferred network exhibits properties and hypotheses that are consistent with current biological knowledge.
van Someren
E P
EP
Department of Mediametics, Delft University of Technology, 2600 GA Delft, The Netherlands. E.P.vanSomeren@tudelft.nl
Vaes
B L T
BL
Steegenga
W T
WT
Sijbers
A M
AM
Dechering
K J
KJ
Reinders
M J T
MJ
D000818:Animals; D002454:Cell Differentiation / Q000502:physiology; D002478:Cells, Cultured; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D051379:Mice; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010006:Osteoblasts / Q000166:cytology* / Q000502:physiology*; D012044:Regression Analysis; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism*
D014157:Transcription Factors