ArticleCell cycle (Georgetown, Tex.)2013
Measurement and modeling of transcriptional noise in the cell cycle regulatory network.
Article in Cell cycle (Georgetown, Tex.), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- Stochastic model of vesicular stomatitis virus replication reveals mutational effects on virion production.PLoS computational biology · 2024Article
- Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants.NPJ systems biology and applications · 2020Article
- Clb3-centered regulations are recurrent across distinct parameter regions in minimal autonomous cell cycle oscillator designs.NPJ systems biology and applications · 2020Article
- Article
- A stochastic model of size control in the budding yeast cell cycle.BMC bioinformatics · 2019Article
- Layers of regulation of cell-cycle gene expression in the budding yeast Saccharomyces cerevisiae.Molecular biology of the cell · 2018Article
- Transcriptional timing and noise of yeast cell cycle regulators-a single cell and single molecule approach.NPJ systems biology and applications · 2018Article
- A Stochastic Model of the Yeast Cell Cycle Reveals Roles for Feedback Regulation in Limiting Cellular Variability.PLoS computational biology · 2016Article
- A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks.PloS one · 2016Article
- Article
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Authors and funding
7 authors.
Funding
Abstract
Fifty years of genetic and molecular experiments have revealed a wealth of molecular interactions involved in the control of cell division. In light of the complexity of this control system, mathematical modeling has proved useful in analyzing biochemical hypotheses that can be tested experimentally. Stochastic modeling has been especially useful in understanding the intrinsic variability of cell cycle events, but stochastic modeling has been hampered by a lack of reliable data on the absolute numbers of mRNA molecules per cell for cell cycle control genes. To fill this void, we used fluorescence in situ hybridization (FISH) to collect single molecule mRNA data for 16 cell cycle regulators in budding yeast, Saccharomyces cerevisiae. From statistical distributions of single-cell mRNA counts, we are able to extract the periodicity, timing, and magnitude of transcript abundance during the cell cycle. We used these parameters to improve a stochastic model of the cell cycle to better reflect the variability of molecular and phenotypic data on cell cycle progression in budding yeast.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.