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The cells adapt to extra- and intra-cellular signals by dynamic orchestration of activities of pathways in the biochemical networks. Dynamic control of the gene expression process represents a major mechanism for pathway activity regulation. Gene expression has thus been routinely measured, most frequently at steady-state mRNA abundance level using micro-array technology. The results are widely used in statistical inference of the structures of underlying biochemical networks, with the assumption that functionally related genes exhibit similar dynamic profiles. Steady-state mRNA abundance, however, is a composite of two factors: transcription rate and mRNA degradation rate. The question being asked here is therefore whether steady-state mRNA abundance or any of two factors is a more informative measurement target for studying network dynamics. The yeast  S. cerevisiae  was used as model organism and transcription rate was chosen out of the two factors in this study, because genome-wide determination of transcription rates has been reported for several physiological processes in this species. Our strategy is to test which one is a better measurement of functional relatedness between genes. The analysis was performed on those  S. cerevisiae  genes that have bacterial orthologs as identified by reciprocal BLAST analysis, so that functional relatedness of a gene pair can be measured by the frequency at which their bacterial orthologs co-occur in the same operon in the collection of bacterial genomes. It is found that transcription rate data is generally a better parameter for functional relatedness than steady state mRNA abundance, suggesting transcription rate data is more informative to use in deciphering the logics used by the cells in dynamic regulation of biochemical network behaviors. The significance of this finding for network and systems biology, as well as biomedical research in general, is discussed.

Comparing Transcription Rate and mRNA Abundance as Parameters for Biochemical Pathway and Network Analysis