ArticleBMC systems biology2015
A cell size- and cell cycle-aware stochastic model for predicting time-dynamic gene network activity in individual cells.
Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Toward predictive engineering of gene circuits.Trends in biotechnology · 2023Review
- Time-resolved microfluidics unravels individual cellular fates during double-strand break repair.BMC biology · 2022Article
- Altered expression response upon repeated gene repression in single yeast cells.PLoS computational biology · 2022Article
- Gene copy number and negative feedback differentially regulate transcriptional variability of segmentation clock genes.iScience · 2022Article
- Multi-scale model suggests the trade-off between protein and ATP demand as a driver of metabolic changes during yeast replicative ageing.PLoS computational biology · 2022Article
- Modeling aging and its impact on cellular function and organismal behavior.Experimental gerontology · 2021Review
- The switch of DNA states filtering the extrinsic noise in the system of frequency modulation.Scientific reports · 2021Article
- Article
- The Reaction of Dimerization by Itself Reduces the Noise Intensity of the Protein Monomer.Scientific reports · 2019Article
- Multi-component gene network design as a survival strategy in diverse environments.BMC systems biology · 2018Article
- Noise reduction as an emergent property of single-cell aging.Nature communications · 2017Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite the development of various modeling approaches to predict gene network activity, a time dynamic stochastic model taking into account real-time changes in cell volume and cell cycle stages is still missing.
resultsHere we present a stochastic single-cell model that can be applied to any eukaryotic gene network with any number of components. The model tracks changes in cell volume, DNA replication, and cell division, and dynamically adjusts rates of stochastic reactions based on this information. By tracking cell division, the model can maintain cell lineage information, allowing the researcher to trace the descendants of any single cell and therefore study cell lineage effects. To test the predictive power of our model, we applied it to the canonical galactose network of the yeast Saccharomyces cerevisiae. Using a minimal set of free parameters and across several galactose induction conditions, the model effectively captured several details of the experimentally-obtained single-cell network activity levels as well as phenotypic switching rates.
conclusionOur model can readily be customized to model any gene network in any of the commonly used cells types, offering a novel and user-friendly stochastic modeling capability to the systems biology field.
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