two factors to. Operons containing highly functionally related genes are, as revealed by comparative genomic analysis of bacterial genomes, more likely to be conserved across species [15,16]. The more an operon is conserved, the more functionally related its genes are. Indeed, the winning method in a comparison of 30 operon prediction methods utilizes cross species conservation as a major predictive component [25]. Therefore, we used normalized co- operon frequency by their bacterial orthologs, as discussed in Materials and Methods, as a measurement of functional relatedness between two S. cerevisiae genes. For all such gene pairs, we already have two correlation coefficients: one between their transcription rate profiles and one between their mRNA abundance profiles. We tested which one of the two correlation coefficients correlate better with the corresponding co-operon frequencies as we increase the minimum co-operon frequency requirement. Two approaches were used based on the observation that distributions of the correlation coefficients are bimodal (see Results section A). We calculated the fraction of the correlation coefficients that are equal or bigger than 0.6, as well as the fraction of the correlation coefficients that are equal or smaller than 20.6. In the first approach, we examined how the fraction of those correlation coefficients, which are equal to or bigger than 0.6, changed along with the elevating co-operon frequency (Fig. 6, top half). In the second approach, we calculated the ratio of the two fractions and examined how the ratio changed along with the elevating co-operon frequency (Fig. 6, bottom half). Across all three experimental conditions and in both approaches, transcription rate data displayed better positive relationship with elevating co-operon frequency than mRNA abundance data. Transcription rate, thus, seems a better indicator of functional relatedness. Discussion Understanding cellular logic in gene expression regulation is crucial for network and systems biology. Biochemical network Network Dynamic Analysis construction can, to some degree, be considered a reverse engineering process - inference of the structure of the networks from their dynamic behavior [26], which is often measured in terms of gene expression levels. Using the right logic/assumption is a prerequisite for such efforts to be successful. Once established, a biochemical network model is useful only when theoretical prediction can be made out of it. Once again, using the right logic is the key to any accurate prediction. Gene expression analysis is, due to its prominent role in cellular dynamics, a critical component of network and systems biology [4]. At the mRNA level, some measurement technologies are hybridization based. Some are sequencing based, measuring mRNA levels as the numbers of times a gene’s tags occur in the sequence set [8,9]. These measurement technologies are less expensive and more developed than those at the protein level. Therefore, gene expression analysis is done, so far, more frequently at the mRNA levels. Most efforts measure steady-state mRNA abundance. Our analysis suggests such datasets should be approached with caution; it is important not to over interpret such data in network analysis and regulatory sequence analysis. While undoubtedly informative, steady-state mRNA abundance data analysis is complicated by the fact that mRNA abundance is determined by two factors: transcription rate and mRNA degradation rate. It is imperative to keep in mind that the discrepancy between transcriptional rate and steady-state mRNA abundance is frequently observed; a gene’s steady-state abundance profile during a physiological process may not be explained by its transcription rate profile. For example, the cells store mRNA molecules in the EGP bodies and the p-bodies (processing bodies) [27,28], so these mRNAs remain abundant even though no new molecules are being produced. However, the precise cellular logic involved in transcription and mRNA degradation regulation remain to be un-covered. At the transcription rate level, an important factor might be to optimally allocate metabolic resources among the set of biochem- Figure 6. Tanscription rate data correlates better with co-operon frequency. Relationship between transcription rate correlation (TR), as well as mRNA abundance level correlation (RA), and co-operon frequency for S. cerevisiae gene pairs whose bacterial orthologs co-occur in the same operons are shown. For each column, the top plot represents the fraction of points above 0.6 as cooperon frequency increases; the bottom plot shows the ratio between the number of points above 0.6 and below 20.6 with increasing cooperon frequency. The bottom plot ends at cooperon frequency where the number of point below 20.6 becomes 0 in the TR data, as the cooperon frequency where the number of point below 20.6 becomes 0 is always higher in the RA data. Both plots are given relative (folds of change) to the first time point. Column A) gives plots for galactose- glucose shift data; column B) gives plots for oxidative stress data; column C) gives plots for osmotic stress data. doi:10.1371/journal.pone.0009908.g006 PLoS ONE | www.plosone.org 6 March 2010 | Volume 5 | Issue 3 | e9908