Systems Level Modeling of the Cell Cycle Using Budding Yeast

Ingalls, Brian; Duncker, Bernard P.; Kim, D.R.; McConkey, Brendan J.

doi:10.1177/117693510700300020

Cited by 7 publications

(6 citation statements)

References 118 publications

(118 reference statements)

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“…The ability to accurately identify yeast cells in different division phases, especially cells in the S phase, is critical in the modeling of cell cycles. 1,5 Currently, the classification of the cell cycle is currently done manually, which is often subjective, inconsistent, and time-consuming. 6 In addition, there is no effective method for collecting and isolating cells in the phase of interest.…”

Section: Introductionmentioning

confidence: 99%

“…1 The budding yeast cells, Saccharomyces cerevisiae, are frequently used as a model species in the study of cell cycles, because the basic elements of the yeast cell's structure are extensively homologous to those in higher plant and animal cells, 2 and the progression of the yeast cell cycle is easily monitored via changes in cell morphology. 3,4 As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Image processing and classification algorithm for yeast cell morphology in a microfluidic chip

et al. 2011

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Abstract. The study of yeast cell morphology requires consistent identification of cell cycle phases based on cell bud size. A computer-based image processing algorithm is designed to automatically classify microscopic images of yeast cells in a microfluidic channel environment. The images were enhanced to reduce background noise, and a robust segmentation algorithm is developed to extract geometrical features including compactness, axis ratio, and bud size. The features are then used for classification, and the accuracy of various machine-learning classifiers is compared. The linear support vector machine, distance-based classification, and k-nearest-neighbor algorithm were the classifiers used in this experiment. The performance of the system under various illumination and focusing conditions were also tested. The results suggest it is possible to automatically classify yeast cells based on their morphological characteristics with noisy and low-contrast images. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image processing and classification algorithm for yeast cell morphology in a microfluidic chip

et al. 2011

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“…The cell cycle is crucial to biological growth, repair, reproduction, and development (Tyson et al, 2013; Chen et al, 2004; Alberts et al, 2002) and is highly conserved among eukaryotes (Alberts et al, 2002). This means that understanding of the cell cycle of S. cerevisiae can provide insight into a variety of cell cycle perturbations including those that occur in human cancer (Ingalls et al, 2007; Chen et al, 2004) and ageing (Jiménez et al, 2015). We aim to create clusters of genes that are co-expressed in the cell cycle, have common regulatory proteins and share a biological function.…”

Section: Methodsmentioning

confidence: 99%

“…The regulatory proteins of the cell cycle are so highly conserved among eukaryotes that many of them function perfectly when transferred from a human cell to a yeast cell (Alberts et al, 2002). This conservation means that a relatively simple eukaryote such as Saccharomyces cerevisiae can provide insight into a variety of cell cycle perturbations including those that occur in human cancer (Ingalls et al, 2007;Chen et al, 2004) and ageing (Jiménez et al, 2015). Budding yeast is particularly attractive for genetic analysis as large numbers of cells may be synchronised in a particular stage of the cell cycle (Juanes, 2017).…”

Section: Introductionmentioning

confidence: 99%

Consensus clustering for Bayesian mixture models

Coleman

Kirk

Wallace

2020

Preprint

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MotivationCluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. However, problems such as choosing the number of clusters and issues with high dimensional data arise consistently. An ensemble approach, such as consensus clustering, can overcome some of the difficulties associated with high dimensional data, frequently exploring more relevant clustering solutions than individual models. Another tool for cluster analysis, Bayesian mixture modelling, has alternative advantages, including the ability to infer the number of clusters present and extensibility. However, inference of these models is often performed using Markov-chain Monte Carlo (MCMC) methods which can suffer from problems such as poor exploration of the posterior distribution and long runtimes. This makes applying Bayesian mixture models and their extensions to ‘omics data challenging. We apply consensus clustering to Bayesian mixture models to address these problems.ResultsConsensus clustering of Bayesian mixture models successfully finds generating structure in our simulation study and captures multiple modes in the likelihood surface. This approach also offers significant reductions in runtime compared to traditional Bayesian inference when a parallel environment is available. We propose a heuristic to decide upon ensemble size and then apply consensus clustering to Multiple Dataset Integration, an extension of Bayesian mixture models for integrative analyses, on three ‘omics datasets for budding yeast. We find clusters of genes that are co-expressed and have common regulatory proteins which we validate using external knowledge, showing consensus clustering can be applied to any MCMC-based clustering method.

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“…[ 40 - 43 ]). For S. cerevisiae in particular, multiple modeling approaches have been applied, based both on network descriptions [ 44 ] and on specific molecular details such as gene expression and biochemical kinetics [ 45 - 47 ] (reviewed in [ 48 ]). Some modeling efforts have been comprehensive, such as the Tyson group’s ordinary differential equation (ODE)-based models [ 45 , 46 ], while others address specific cell-cycle phenomena, such as the links between cell size and cycle progression [ 49 , 50 ].…”

Section: Introductionmentioning

confidence: 99%

A quantitative model of the initiation of DNA replication in Saccharomyces cerevisiae predicts the effects of system perturbations

Gidvani

Sudmant

et al. 2012

BMC Syst Biol

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BackgroundEukaryotic cell proliferation involves DNA replication, a tightly regulated process mediated by a multitude of protein factors. In budding yeast, the initiation of replication is facilitated by the heterohexameric origin recognition complex (ORC). ORC binds to specific origins of replication and then serves as a scaffold for the recruitment of other factors such as Cdt1, Cdc6, the Mcm2-7 complex, Cdc45 and the Dbf4-Cdc7 kinase complex. While many of the mechanisms controlling these associations are well documented, mathematical models are needed to explore the network’s dynamic behaviour. We have developed an ordinary differential equation-based model of the protein-protein interaction network describing replication initiation.ResultsThe model was validated against quantified levels of protein factors over a range of cell cycle timepoints. Using chromatin extracts from synchronized Saccharomyces cerevisiae cell cultures, we were able to monitor the in vivo fluctuations of several of the aforementioned proteins, with additional data obtained from the literature. The model behaviour conforms to perturbation trials previously reported in the literature, and accurately predicts the results of our own knockdown experiments. Furthermore, we successfully incorporated our replication initiation model into an established model of the entire yeast cell cycle, thus providing a comprehensive description of these processes.ConclusionsThis study establishes a robust model of the processes driving DNA replication initiation. The model was validated against observed cell concentrations of the driving factors, and characterizes the interactions between factors implicated in eukaryotic DNA replication. Finally, this model can serve as a guide in efforts to generate a comprehensive model of the mammalian cell cycle in order to explore cancer-related phenotypes.

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Systems Level Modeling of the Cell Cycle Using Budding Yeast

Cited by 7 publications

References 118 publications

Image processing and classification algorithm for yeast cell morphology in a microfluidic chip

Image processing and classification algorithm for yeast cell morphology in a microfluidic chip

Consensus clustering for Bayesian mixture models

A quantitative model of the initiation of DNA replication in Saccharomyces cerevisiae predicts the effects of system perturbations

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