Distinct Interactions Select and Maintain a Specific Cell Fate
TLDR
A quantitative single-cell analysis of commitment dynamics during the mating-mitosis switch in budding yeast shows that specification and maintenance of a cellular state are performed by distinct interactions, which are likely a consequence of disparate reaction rates and may be a general feature of the interlinked regulatory networks responsible for selecting cell fates.About:
This article is published in Molecular Cell.The article was published on 2011-08-19 and is currently open access. It has received 139 citations till now. The article focuses on the topics: Cyclin A & Cell fate determination.read more
Citations
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Control of cell cycle transcription during G1 and S phases
TL;DR: The complex molecular mechanisms that control the temporal order of transcriptional activation and inactivation, determine distinct functional subgroups of genes and link cell cycle-dependent transcription to DNA replication stress in yeast and mammals are revealed.
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Cell Size Control in Yeast
TL;DR: Examination of size-sensing models based on spatial gradients and molecular titration, coupled with elucidation of the pathways responsible for nutrient-modulated target size, may reveal the fundamental principles of eukaryotic cell size control.
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Dilution of the cell cycle inhibitor Whi5 controls budding-yeast cell size
TL;DR: At its most fundamental level, size control in budding yeast results from the differential scaling of Cln3 and Whi5 synthesis rates with cell size, and shows that differential size-dependency of protein synthesis can provide an elegant mechanism to coordinate cellular functions with growth.
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Targeting the cyclin-dependent kinases (CDK) 4/6 in estrogen receptor-positive breast cancers
TL;DR: Recent preclinical and phase I/II clinical studies using a novel, oral, reversible CDK4/6 inhibitor, palbociclib (PD-0332991), have validated the role of CDK 4/6 as a potential target in estrogen receptor-positive (ER+) breast cancers.
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Acetyl-CoA induces transcription of the key G1 cyclin CLN3 to promote entry into the cell division cycle in Saccharomyces cerevisiae
Lei Shi,Benjamin P. Tu +1 more
TL;DR: It is shown that a central metabolite of glucose catabolism, acetyl-CoA, induces CLN3 transcription by promoting the acetylation of histones present in its regulatory region.
References
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Book
Applied Logistic Regression
David W. Hosmer,Stanley Lemeshow +1 more
TL;DR: Hosmer and Lemeshow as discussed by the authors provide an accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets.
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Applied Logistic Regression.
TL;DR: Applied Logistic Regression, Third Edition provides an easily accessible introduction to the logistic regression model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
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CDK inhibitors: positive and negative regulators of G1-phase progression
TL;DR: This work challenges previous assumptions about how the G1/S transition of the mammalian cell cycle is governed, helps explain some enigmatic features of cell cycle control that also involve the functions of the retinoblastoma protein (Rb) and the INK4 proteins, and changes the thinking about how either p16 loss or overexpression of cyclin D-dependent kinases contribute to cancer.
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Genetic Control of the Cell Division Cycle in Yeast
TL;DR: Two features which distinguish the cell cycle of Saccharomyces cerevisiae from most other eukaryotes are particularly useful for an analysis of the gene functions that control the cell division cycle.
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Systematic identification of cell cycle-dependent yeast nucleocytoplasmic shuttling proteins by prediction of composite motifs
TL;DR: The successful prediction of CDK1-regulated nucleocytoplasmic shuttling proteins is reported using a prediction system for nuclear localization signals (NLSs) and the application of this strategy to other functional linear motifs should be useful in systematic studies of protein–protein networks.