To investigate deeper this scheme, Simon et al. (12) ran a set of genome-wide location experiments to find out how the cell cycle transcription program is controlled by each of the known cell cycle transcription activators. By combination of their binding data and previous studies, Simon et al. identified and extended model of transcriptional control of cyclin/CDK genetic network, where each group of transcription factors controls key cell cycle regulators needed for cell cycle progression (Figure 1A). Since then, no systematic experimental study on the control of cyclins network has been performed. In their paper, Simon et al. suggests a new model of transcriptional control of cell cycle regulators that define cyclins genetic network. Based on the location analysis, they found that SBF and MBF complexes control transcription of G1/S cyclin genes, but also regulate expression of the G2/M cyclin Clb2, which promotes entry into mitosis. SBF and MBF also regulate the transcription of the transcription factor Ndd1, which also binds the CLB2 promoter. Thus, SBF, MBF and Ndd1 ultimately collaborate to regulate transcription of the CLB2 gene. SBF and MBF therefore regulate genes necessary for the transition through G1/S, as well as genes whose products set the stage for further progression through the cell cycle. Their data also revealed that the G2/M activators (Mcm1/Fkh2/Ndd1) bind genes whose expression is necessary for both entry into and exit from mitosis. The G2/M activators bind and regulate transcription of CLB2, whose product is necessary to enter mitosis. These genes also set the stage for exit from mitosis by regulating the gene encoding Cdc20, an activator of the APC, which targets the APC to degrade Pds1 and thus initiate chromosome separation. Cdc20-activated APC also degrades Clb5 and thus enables Cdc14 to promote the transcription and activation of Sic1 and to initiate the degradation of Clb2. In addition, the G2/M activators Mcm1/Fkh2/Ndd1 regulate transcription of SPO12, which encodes a protein that also regulates mitotic exit. The M/G1 transcriptional regulators (Mcm1, Ace2 and Swi5) bind genes that are key to entering and progressing through G1. Swi5 binds to the SIC1 promoter, and all three transcriptional regulators bind to the CLN3 promoter. Sic1 inhibits Clb–Cdc28 during mitosis, thus facilitating exit from mitosis. Cln3–Cdc28 activates SBF and MBF in late G1, thus setting the stage for another cell cycle circuit (12).

Later a systematic study dealing with binding of extended number of transcriptional regulators to DNA was performed (13). Using genome-wide location analysis with epitope tagging Harbison et al. determined the genomic occupancy of 203 DNA-binding transcriptional regulators including those identified as members of the cyclins genetic network. Similar work that focused on conservation of transcriptional regulators across different saccharomyces species was performed recently (1).

The above-mentioned simulation procedure was used to infer topology and interactions within this network. For this purpose, microarray experiments were used that covered two cell cycles of yeast that had been made in triplicate (2). Original data were stored as log base 10 values. For further use, the data were exponentiated and individual profiles corresponding to the same gene from the three parallel experiments were averaged and standard deviation for each time point was calculated. Binding interactions among members of this network were identified from ChIP experiments (13) and have been assembled in Table 1. Table 1 served for the design of necessary simulations, modeling both single regulators and two regulators acting together. The number of necessary simulations is given by the Table 1 column labeled ‘comb’. In the case of genes for which three regulators were suggested by Simon et al. (NDD1, SWI5, ACE2, SWI4, CLN3 and CLB2), we also performed a simulation for three regulators. Altogether 125 simulations were made. For each simulation, model parameter estimates were computed (see ‘Materials and Methods’ section), and a statistical test was used to compare average simulated profiles with the average experimentally measured profile. Those simulated profiles passing the statistical test were selected, and the values of the parameters w_ij were used to infer the regulators of the given gene and their activity (activator or repressor). The results are summarized in Table 2.

A comparison of Tables 1 and ​and22 shows the differences between the predictions made solely based on genome-wide location data and data computed using the simulation model. A comparison of the two approaches is given in Table 3. Table 3 shows the following: (i) there was a partial overlap between the genome-wide location data analysis and the results of simulation experiments. (ii) In some cases, the statistical test for the similarity between the simulated and measured target gene expression profiles was satisfied by more than one combination of regulators. (iii) The ‘cc’ column indicates a high correlation between simulated and measured mean expression profiles, a correlation that is always >0.9. The alternative combinations of regulators must be considered equal and cannot be excluded based on differences in the correlation. All are statistically significant, and all are very highly correlated. Interactions suggested by genome-wide location analysis (12) and those found by the simulations are depicted in Figure 1. Out of 17 total regulated genes, six of them were found to have the same regulators by genome-wide location analysis and simulations, seven were found to have different regulators and the remaining four genes indicated partial regulator overlap between the two types of analyses. In the following paragraph, individual regulatory interactions and differences between the results of simulation and genome-wide location analysis are discussed. The kinetic behavior of transcription of the selected genes was modeled with high accuracy, typically yielding a correlation coefficient between measured and simulated profiles higher than 0.98 (Table 3), with statistically significant similarity between measured and simulated expression profiles for 24 out of 25 total measured points.

In several cases, regulation suggested by genome-wide location analysis for two or more regulators proved redundant from the kinetic point of view. For NDD1, genome-wide location analysis suggested three regulators (Swi6/Swi4/Mbp1), whereas for the simulation of Ndd1 kinetics, Swi6 alone was sufficient. Similarly, for SWI4, suggested control is by Mcm1/Swi6/Swi4/Mbp1, whereas for the simulation, the regulators Mcm1/Swi4/Mbp1 were sufficient (Table 3). When simulation and experimental variance were used to confirm or reject control by different combinations of regulators for a given target gene, several alternative control patterns for one target gene could be discerned; e.g. for the case of SWI5, control by a triplet Fkh2/Ndd1/Mcm1 was statistically equivalent to control by pairs Fkh2/Mcm1 and Fkh2/Ndd1 (a similar result was obtained for CLB6, SWI4 and CLN1; Table 3). These individual regulator combinations were indistinguishable from each other, and they must be considered equivalent. Such results constitute alternative hypotheses which have to be confirmed independently.

Two complexes SBF (Swi4/Swi6) and MBF (Mbp1/Swi6) are supposed to control cyclins CLN1,2 and CLB5,6 respectively (18) and NDD1 together with Mbp1 (12). CLN1,2 was indeed found to be regulated by SBF, alternatively by Swi4/Mbp1 pair. In accordance with the binding experiments SWI4/MBP1 (or alternatively SBF) was found to control also CLB6. Simulation of CLB6 control by MBF suggested by literature showed low correlation with experimental time series (Pearson correlation=0.34). For the control of NDD1, Swi6 only was found to be sufficient although the simulation of the control by MBF showed to be highly correlated with experimental time series as well (Pearson correlation=0.93, see Supplementary Dataset).

When the same set of regulators was identified by both experiments (simulation versus genome-wide location analysis), some regulators located by simulation were required to serve as repressors. For example, take ACE2. Both experiments suggest three regulators (Fkh2/Ndd1/Mcm1), but simulation found that Mcm1 must act as repressor, or else the Ace2 expression profile could not be modeled successfully. Similarly for SWI5 (regulators Fkh2/Ndd1/Mcm1), Ndd1 and Mcm1 had to act as repressors to simulate the expression profile of Swi5. Genome-wide location analysis does not address this issue. After searching the literature, we found that for this case, no one has investigated the regulators’ activity as an activator or repressor.

For four particular genes (SIC1, FAR1, CLN2 and CLN3; Table 3), no combination of regulators suggested by ChIP-chip experiments could simulate the kinetics of the target genes with sufficient confidence. Control suggested by genome-wide location analysis was thus not confirmed by simulation. This observation indicates that the control of these genes may follow different pathway than purely transcriptional.

The differences between binding and simulation experiments could be caused principally by two items: measurement inaccuracy and a type of control different than the transcriptional one. Time series of gene expression are usually measured using microarrays, and number of replicates usually does not exceed three. When the measurement covers whole cell cycle or two (as for the data used here), the synchrony of the population decays with time, leading to increase of variance with time. As a result, the average measured expression profile may differ from the real one. As the simulation is designed to fit the experimental data, any error in them distorts final conclusion. Increase of the measurement reproducibility will also increase reliability of the predictions based on the simulations.

The simulation used here, suppose transcriptional control and investigates that this type of control is possible from the kinetic point of view. When other type of control is involved, then the simulation cannot find the correct solution. The same argument holds for interpretation of the binding experiments which say that the regulator binds the promoter, but cannot discover posttranscriptional or other events and cannot even say whether the binding results in transcription. If, e.g. phosphorylation is involved in transcriptional control, the binding experiments’ results, combined with the simulation results, will fail to discover the real control mechanism. The additional information, the phosphorylation, is missing. Control of CLB2 can serve as an example. CLB2 was found to be controlled by Mcm1 together with Fkh2 and co-activator Ndd1. But Clb2-Cdc28 phosphorylates Ndd1, which is important for recruitment to the CLB2 gene promoter, and phosphorylates Fkh2, which enhances the interaction of Fkh2 with Ndd1 (18). The simulation found that only the pair Fkh2, Ndd1 was sufficient to control CLB2 gene, with Ndd1 acting as repressor. Therefore, the multiple phosphorylations involved in the control were replaced by kinetically acceptable alternative. If the current knowledge indicates that the control of the given gene is transcriptional and the results of kinetic simulation differ from those measured statically (as the binding experiments) it is an indication that the process follows different pathway then suggested, i.e. most probably some information is missing. Therefore, such situations have to be carefully checked and additional experiments have to be designed that would investigate involvement of other possible control mechanisms. As the binding experiments usually indicate several possible combinations of regulators, simulation can choose those combinations which are kinetically possible. Role of the modeling in these cases is in questioning or confirmation of the current knowledge.