A significant fraction of the Saccharomyces cerevisiae genome is transcribed periodically during the cell division cycle1,2, suggesting that properly timed gene expression is important for regulating cell cycle events. Genomic analyses of transcription factor localization and expression dynamics suggest that a network of sequentially expressed transcription factors could control the temporal program of transcription during the cell cycle3. However, directed studies interrogating small numbers of genes indicate that their periodic transcription is governed by the activity of cyclin-dependent kinases (CDKs)4. To determine the extent to which the global cell cycle transcription program is controlled by cyclin/CDK complexes, we examined genome-wide transcription dynamics in budding yeast mutant cells that do not express S-phase and mitotic cyclins. Here we show that a significant fraction of periodic genes were aberrantly expressed in the cyclin mutant. Surprisingly, although cells lacking cyclins are blocked at the G1/S border, nearly 70% of periodic genes continued to be expressed periodically and on schedule. Our findings reveal that while CDKs play a role in the regulation of cell cycle transcription, they are not solely responsible for establishing the global periodic transcription program. We propose that periodic transcription is an emergent property of a transcription factor network that can function as a cell cycle oscillator independent of, and in tandem with, the CDK oscillator.The biochemical oscillator controlling periodic events during the cell cycle is centered on the activity of cyclin-dependent kinases (CDKs) (reviewed in ref. 5). The cyclin/CDK oscillator governs the major events of the cell cycle, and in embryonic systems this oscillator functions in the absence of transcription, relying only on maternal stockpiles of mRNAs and proteins. CDKs are also thought to act as the central oscillator in somatic cells and yeast, and directed

We present a method for jointly learning dynamic models of transcriptional regulatory networks from gene expression data and transcription factor binding location data. Models are automatically learned using dynamic Bayesian network inference algorithms; joint learning is accomplished by incorporating evidence from gene expression data through the likelihood, and from transcription factor binding location data through the prior. We propose a new informative structure prior with two advantages. First, the prior incorporates evidence from location data probabilistically, allowing it to be weighed against evidence from expression data. Second, the prior takes on a factorable form that is computationally efficient when learning dynamic regulatory networks. Results obtained from both simulated and experimental data from the yeast cell cycle demonstrate that this joint learning algorithm can recover dynamic regulatory networks from multiple types of data that are more accurate than those recovered from each type of data in isolation.

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Due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, time-series measurements from synchronized cell populations do not reflect the underlying dynamics of cell-cycle processes. Here, we present a branching process deconvolution algorithm that learns a more accurate view of dynamic cell-cycle processes, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Although applicable to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote Saccharomyces cerevisiae. Our method more sensitively detects cellcycle-regulated transcription and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in early G1 in a daughter-specific manner.O ne of the most fundamental processes in biology is the cell cycle, the intricate progression of events necessary for a cell's division. To better understand how cell-cycle events are regulated, studies in many organisms have monitored the dynamics of various molecular species (e.g., transcript levels, protein levels, nucleosome positions) throughout the cell cycle. Ideally, the dynamics of these species would be studied within individual cells traversing the cell cycle.Unfortunately, current technology enables accurate, genomewide quantification of many molecular species only in populations of cells. To provide insight into the dynamics of cell-cycle processes, the cells in such a population should be as synchronized as possible as they progress through the cell division cycle. To effect this synchrony, cells are arrested or selected at one stage of the cell cycle and then released to progress through subsequent division cycles. Molecular species can then be measured in the population at various time points after release (1-5).Measurements of cell populations would not be substantially different from average measurements of individual cells if the cells in the population were always perfectly synchronized. However, perfect cell synchrony is neither attainable at synchronization nor maintainable after release. Cells exhibit variability even at the time of release, and synchrony deteriorates further over time because individual cells progress through the cell cycle at different rates. Moreover, asymmetric cell division is a major source of synchrony loss in many kinds of cells and especially in budding yeast (6-10). After yeast cell division, newborn daughter cells are smaller than their mothers, and the cycle period of daughters is significantly longer than that of mothers. This is most likely due to mechanisms-not yet we...

Abstract. Recent advances in high-throughput experimental techniques have enabled the production of a wealth of protein interaction data, rich in both quantity and variety. While the sheer quantity and variety of data present special difficulties for modeling, they also present unique opportunities for gaining insight into protein behavior by leveraging multiple perspectives. Recent work on the modularity of protein interactions has revealed that reasoning about protein interactions at the level of domain interactions can be quite useful. We present PROCTOR, a learning algorithm for reconstructing the internal topology of protein complexes by reasoning at the domain level about both direct protein interaction data (Y2H) and protein co-complex data (AP-MS). While other methods have attempted to use data from both these kinds of assays, they usually require that co-complex data be transformed into pairwise interaction data under a spoke or clique model, a transformation we do not require. We apply PROCTOR to data from eight highthroughput datasets, encompassing 5,925 proteins, essentially all of the yeast proteome. First we show that PROCTOR outperforms other algorithms for predicting domain-domain and protein-protein interactions from Y2H and AP-MS data. Then we show that our algorithm can reconstruct the internal topology of AP-MS purifications, revealing known complexes like Arp2/3 and RNA polymerase II, as well as suggesting new complexes along with their corresponding topologies.