Genetic network analysis in light of massively parallel biological data acquisition.

Complementary DNA microarray and high density oligonucleotide arrays opened the opportunity for massively parallel biological data acquisition. Application of these technologies will shift the emphasis in biological research from primary data generation to complex quantitative data analysis. Reverse engineering of time-dependent gene-expression matrices is amongst the first complex tools to be developed. The success of reverse engineering will depend on the quantitative features of the genetic networks and the quality of information we can obtain from biological systems. This paper reviews how the (1) stochastic nature, (2) the effective size, and (3) the compartmentalization of genetic networks as well as (4) the information content of gene expression matrices will influence our ability to perform successful reverse engineering.