Current　methods　for　the　detection　of　differential　gene　expression　focus　on　finding　individual　genes　that　may　be　responsible　for　certain　diseases　or　external　irritants.　However,　for　common　genetic　diseases,　multiple　genes　and　their　interactions　should　be　understood　and　treated　together　during　the　exploration　of　disease　causes　and　possible　drug　design.　The　present　study　focuses　on　analyzing　the　dynamic　patterns　of　co-regulated　modules　during　biological　progression　and　determining　those　having　remarkably　varying　activities,　using　the　yeast　cell　cycle　as　a　case　study.　We　first　constructed　dynamic　active　protein-protein　interaction　networks　by　modeling　the　activity　of　proteins　and　assembling　the　dynamic　co-regulation　protein　network　at　each　time　point.　The　dynamic　active　modules　were　detected　using　a　method　based　on　the　Bayesian　graphical　model　and　then　the　modules　with　the　most　varied　dispersion　of　clustering　coefficients,　which　could　be　responsible　for　the　dynamic　mechanism　of　the　cell　cycle,　were　identified.　Comparison　of　results　from　our　functional　module　detection　with　the　state-of-art　functional　module　detection　methods　and　validation　of　the　ranking　of　activities　of　functional　modules　using　GO　annotations　demonstrate　the　efficacy　of　our　method　for　narrowing　the　scope　of　possible　essential　responding　modules　that　could　provide　multiple　targets　for　biologists　to　further　experimentally　validate.