Yeast Cell Cycle Research Articles

Towards a Yeast Cell Cycle Hybrid Model: Structural Characterization of Recombinant Whi5, a Transcriptional Regulator of Cycle Progression in Saccharomyces cerevisiae

Towards a Yeast Cell Cycle Hybrid Model: Structural Characterization of Recombinant Whi5, a Transcriptional Regulator of Cycle Progression in Saccharomyces cerevisiae

Towards a yeast cell cycle Sys-bio model: CK2 activity is modulated by growth rate in Saccharomyces cerevisiae

Towards a yeast cell cycle Sys-bio model: CK2 activity is modulated by growth rate in Saccharomyces cerevisiae

Regulation of G1 Cell Cycle Progression: Distinguishing the Restriction Point from a Nutrient-Sensing Cell Growth Checkpoint(s)

Most genetic changes that promote tumorigenesis involve dysregulation of G1 cell cycle progression. A key regulatory site in G1 is a growth factor-dependent restriction point (R) where cells commit to mitosis. In addition to the growth factor-dependent "R," which maps to a site about 3.5 hours after mitosis, there is another checkpoint later in G1 that is dependent on nutritional sufficiency that has also been referred to as R. However, this second site in late G1 can be distinguished both temporally and genetically from R. We are proposing that the late G1 regulatory site be more appropriately referred to as a "cell growth" checkpoint to distinguish it from R. This checkpoint, which likely has an evolutionary relationship to the yeast cell cycle checkpoint START, is regulated by signals governed by mTOR, the mammalian target of rapamycin. This review summarizes evidence that distinguishes R from the proposed cell growth checkpoint. Since both checkpoints are dysregulated in most, if not all, human cancers, distinguishing between these 2 distinct G1 regulatory checkpoints has significance for rational therapeutic strategies targeting oncogenic signals.

Towards a yeast cell cycle Sysbio model: Snf1/AMPK promotes S-phase entrance by controlling CLB5 transcription in budding yeast

Towards a yeast cell cycle Sysbio model: Snf1/AMPK promotes S-phase entrance by controlling CLB5 transcription in budding yeast

Towards a yeast cell cycle hybrid model: A new experimental model to study the link between PKA activity and cell cycle in budding yeast

Towards a yeast cell cycle hybrid model: A new experimental model to study the link between PKA activity and cell cycle in budding yeast

Systems Biology of the Yeast Cell Cycle

Cell counts and viral load serve as major clinical indicators to provide treatment in the course of a viral infection. Monitoring these markers in patients can be expensive and some of them are not feasible to perform. An alternative solution to this problem is the observer based estimation. Several observer schemes require the previous knowledge of the model and parameters, such condition is not achievable for some applications. A linear output assumption is required in the majority of the current works. Nevertheless, the output of the system can be a nonlinear combination of the state variables. This paper presents a discrete-time neural observer for non-linear systems with a non-linear output; the mathematical model is assumed to be unknown. The observer is trained on-line with the extended Kalman filter (EKF)-based algorithm and the respective stability analysis based on the Lyapunov approach is addressed. We applied different observers to the estimation problem in HIV infection; that is state estimation of the viral load, and the number of infected and non-infected CD4+ T cells. Simulation results suggest a good performance of the proposed neural observer and the applicability to biological systems.

Towards a yeast cell cycle hybrid model: global transcriptional response analysis to the dosage of the FAR1 gene encoding aprotein involed in setting the cells size at S phase initiation

Towards a yeast cell cycle hybrid model: global transcriptional response analysis to the dosage of the FAR1 gene encoding aprotein involed in setting the cells size at S phase initiation

Towards a yeast cell cycle Sys-bio model. Regulation of family 3 ubiquitin-conjugating enzymes by phosphorylation: a molecular dynamics investigation

Dietary polyphenols present in Punica granatum (pomegranate), such as ellagitannins and ellagic acid (EA) have shown to exert anti-inflammatory and antioxidant properties. This study was designed to evaluate the effects of a dietary EA-enriched pomegranate extract (PE) in a murine chronic model of Cronh's disease (CD). Colonic injury was induced by intracolonic instillation of trinitrobenzensulfonic acid (TNBS). Rats were fed with different diets during 30 days before TNBS instillation and 2 weeks before killing: (i) standard, (ii) PE 250 mg/kg/day, (iii) PE 500 mg/kg/day, (iv) EA 10 mg/kg/day and (v) EA 10 mg/kg/day enriched-PE 250 mg/kg/day. Inflammation response was assessed by histology and MPO activity and TNF-α production. Besides, colonic expressions of iNOS, COX-2, p38, JNK, pERK1/2 MAPKs, IKBα and nuclear p65 NF-κB were studied by western blotting. MPO activity and the TNF-α levels were significantly reduced in dietary fed rats when compared with TNBS group. Similarly, PE and an EA-enriched PE diets drastically decreased COX-2 and iNOS overexpression, reduced MAPKs phosporylation and prevented the nuclear NF-κB translocation. Dietary supplementation of EA contributes in the beneficial effect of PE in this experimental colitis model and may be a novel therapeutic strategy to manage inflammatory bowel disease (IBD).

Towards a yeast cell cycle hybrid model: network analysis for model building of the coordination between cell growth and division

Towards a yeast cell cycle hybrid model: network analysis for model building of the coordination between cell growth and division

Power saving experiments for large-scale global optimisation

Green computing, an emerging field of research that seeks to reduce excess power consumption in high-performance computing, is gaining popularity among researchers. Research in this field often relies on simulation or only uses a small cluster, typically 8 or 16 nodes, because of the lack of hardware support. In contrast, System G at Virginia Tech is a 2592 processor supercomputer equipped with power-aware components suitable for large-scale green computing research. DIRECT is a deterministic global optimisation algorithm, implemented in the mathematical software package VTDIRECT95. This paper explores the potential energy savings for the parallel implementation of DIRECT, called pVTdirect, when used with a large-scale computational biology application, parameter estimation for a budding yeast cell cycle model, on System G. Two power-aware approaches for pVTdirect are developed and compared against the CPUSPEED power saving system tool. The results show that knowledge of the parallel workload of the underlying application is beneficial for power management.

Cdc14: a highly conserved family of phosphatases with non-conserved functions?

CDC14 was originally identified by L. Hartwell in his famous screen for genes that regulate the budding yeast cell cycle. Subsequent work showed that Cdc14 belongs to a family of highly conserved dual-specificity phosphatases that are present in a wide range of organisms from yeast to human. Human CDC14B is even able to fulfill the essential functions of budding yeast Cdc14. In budding yeast, Cdc14 counteracts the activity of cyclin dependent kinase (Cdk1) at the end of mitosis and thus has important roles in the regulation of anaphase, mitotic exit and cytokinesis. On the basis of the functional conservation of other cell-cycle genes it seemed obvious to assume that Cdc14 phosphatases also have roles in late mitosis in mammalian cells and regulate similar targets to those found in yeast. However, analysis of the human Cdc14 proteins (CDC14A, CDC14B and CDC14C) by overexpression or by depletion using small interfering RNA (siRNA) has suggested functions that are quite different from those of ScCdc14. Recent studies in avian and human somatic cell lines in which the gene encoding either Cdc14A or Cdc14B had been deleted, have shown - surprisingly - that neither of the two phosphatases on its own is essential for viability, cell-cycle progression and checkpoint control. In this Commentary, we critically review the available data on the functions of yeast and vertebrate Cdc14 phosphatases, and discuss whether they indeed share common functions as generally assumed.

Fast live simultaneous multiwavelength four-dimensional optical microscopy

Live fluorescence microscopy has the unique capability to probe dynamic processes, linking molecular components and their localization with function. A key goal of microscopy is to increase spatial and temporal resolution while simultaneously permitting identification of multiple specific components. We demonstrate a new microscope platform, OMX, that enables subsecond, multicolor four-dimensional data acquisition and also provides access to subdiffraction structured illumination imaging. Using this platform to image chromosome movement during a complete yeast cell cycle at one 3D image stack per second reveals an unexpected degree of photosensitivity of fluorophore-containing cells. To avoid perturbation of cell division, excitation levels had to be attenuated between 100 and 10,000× below the level normally used for imaging. We show that an image denoising algorithm that exploits redundancy in the image sequence over space and time allows recovery of biological information from the low light level noisy images while maintaining full cell viability with no fading.

Reliability of Transcriptional Cycles and the Yeast Cell-Cycle Oscillator

A recently published transcriptional oscillator associated with the yeast cell cycle provides clues and raises questions about the mechanisms underlying autonomous cyclic processes in cells. Unlike other biological and synthetic oscillatory networks in the literature, this one does not seem to rely on a constitutive signal or positive auto-regulation, but rather to operate through stable transmission of a pulse on a slow positive feedback loop that determines its period. We construct a continuous-time Boolean model of this network, which permits the modeling of noise through small fluctuations in the timing of events, and show that it can sustain stable oscillations. Analysis of simpler network models shows how a few building blocks can be arranged to provide stability against fluctuations. Our findings suggest that the transcriptional oscillator in yeast belongs to a new class of biological oscillators.

Regulatory networks and connected components of the neutral space

The functioning of a living cell is largely determined by the structure of its regulatory network, comprising non-linear interactions between regulatory genes. An important factor for the stability and evolvability of such regulatory systems is neutrality – typically a large number of alternative network structures give rise to the necessary dynamics. Here we study the discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004] and the set of networks capable of reproducing it, which we call functional. Among these, the empirical yeast wildtype network is close to optimal with respect to sparse wiring. Under point mutations, which establish or delete single interactions, the neutral space of functional networks is fragmented into ≈ 4.7 × 108 components. One of the smaller ones contains the wildtype network. On average, functional networks reachable from the wildtype by mutations are sparser, have higher noise resilience and fewer fixed point attractors as compared with networks outside of this wildtype component.

Effect of 2.45 mT sinusoidal 50 Hz magnetic field on Saccharomyces cerevisiae strains deficient in DNA strand breaks repair

Purpose: To investigate whether extremely-low frequency magnetic field (MF) exposure produce alterations in the growth, cell cycle, survival and DNA damage of wild type (wt) and mutant yeast strains.Materials and methods: wt and high affinity DNA binding factor 1 (hdf1), radiation sensitive 52 (rad52), rad52 hdf1 mutant Saccharomyces cerevisiae strains were exposed to 2.45 mT, sinusoidal 50 Hz MF for 96 h. MF was generated by a pair of Helmholtz coils. During this time the growth was monitored by measuring the optical density at 600 nm and cell cycle evolution were analysed by microscopic morphological analysis. Then, yeast survival was assayed by the drop test and DNA was extracted and electrophoresed.Results: A significant increase in the growth was observed for rad52 strain (P = 0.005, Analysis of Variance [ANOVA]) and close to significance for rad52 hdf1 strain (P = 0.069, ANOVA). In addition, the surviving fraction values obtained for MF-exposed samples were in all cases less than for the controls, being the P value obtained for the whole set of MF-treated strains close to significance (P = 0.066, Student's t-test). In contrast, the cell cycle evolution and the DNA pattern obtained for wt and the mutant strains were not altered after exposure to MF.Conclusions: The data presented in the current report show that the applied MF (2.45 mT, sinusoidal 50 Hz, 96 h) induces alterations in the growth and survival of S. cerevisiae strains deficient in DNA strand breaks repair. In contrast, the MF treatment does not induce alterations in the cell cycle and does not cause DNA damage.

Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model

We have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP-chip and Expression data), an algorithm for learning a transcriptional network from ChIP-chip and expression data. The network is specified by a binary matrix of transcription factor (TF)-gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation. We define a likelihood of a network, and then search for the network optimizing the likelihood. We applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle. It found 12 condition-specific TFs and 15 modules each of which is highly represented with functions related to particular phases of cell-cycle regulation. Our algorithm is available at http://linus.nci.nih.gov/Data/YounA/LeTICE.zip

Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle

The effort to identify genes with periodic expression during the cell cycle from genome-wide microarray time series data has been ongoing for a decade. However, the lack of rigorous modeling of periodic expression as well as the lack of a comprehensive model for integrating information across genes and experiments has impaired the effort for the accurate identification of periodically expressed genes. To address the problem, we introduce a Bayesian model to integrate multiple independent microarray data sets from three recent genome-wide cell cycle studies on fission yeast. A hierarchical model was used for data integration. In order to facilitate an efficient Monte Carlo sampling from the joint posterior distribution, we develop a novel Metropolis–Hastings group move. A surprising finding from our integrated analysis is that more than 40% of the genes in fission yeast are significantly periodically expressed, greatly enhancing the reported 10–15% of the genes in the current literature. It calls for a reconsideration of the periodically expressed gene detection problem.

Snf1/AMPK promotes S-phase entrance by controlling CLB5 transcription in budding yeast

The Saccharomyces cerevisiae Snf1 protein kinase has been reported to be required for adaptation to glucose limitation and for growth on non-fermentable carbon sources. Here we present novel findings indicating that Snf1, the key regulator of cellular energy, is also involved in yeast cell cycle control. The lack of Snf1 α-catalytic subunit down-regulates the growth rate and CLB5 expression, delaying Sld2 phosphorylation and G1/S transition, in cells grown in 2%, but not in 5% glucose. A non-phosphorylatable Snf1 rescues the slow growth phenotype, whereas a wild type or a phosphomimetic mutant is required to rescue growth rate and the G1/S delay. Using either Snf1 or Swi6 as a bait, a specific interaction of Snf1 with Swi6, the regulatory subunit of MBF, was detected. In conclusion, this report describes a previously unrecognized role for Snf1 in transcriptional modulation of the G1 to S transition, differing from the reported AMPK role in controlling the G1/S transition in multicellular eukaryotes.

Measurement variation determines the gene network topology reconstructed from experimental data: a case study of the yeast cyclin network

Inference of the topology of gene regulatory networks from experimental data is one of the primary challenges of systems biology. In an example of a genetic network of cyclins in the yeast cell cycle, we analyzed static genome-wide location data together with microarray kinetic measurements using a recurrent neural network-based model of gene expression and a newly developed, unbiased algorithm based on evolutionary programming principles. The modeling and simulation of gene expression dynamics identified cyclin genetic networks that were active during the cell cycle. We document that because there is inherent experimental variation, it is not possible to identify a single genetic network, only a set of equivalent networks with the same probability of occurrence. Analysis of these networks showed that each target gene was controlled by only a few regulators and that the control was robust. These results led to the reformulation of the cyclin genetic network in the yeast cell cycle as previously published. The analysis shows that with the methodologies that are currently available, it is not possible to predict only one genetic network; rather, we must work with the hypothesis of multiple, equivalent networks. Chromatin immunoprecipitation (ChIP)-on-chip experiments are not sufficient to predict the functional networks that are active during an investigated process. Such predictions must be considered as only potential, and their actual realization during particular cellular processes must be identified by incorporating both kinetic and other types of data.

Cdc14 in good repair

Cdc14 is an essential regulator of the yeast cell cycle, but its vertebrate homologues appear to be surprisingly dispensable, [Mocciaro et al.][1] report. They are required for efficient DNA repair, however. ![Figure][2] Three hours after irradiation, cells lacking Cdc14A (middle) or Cdc14B