ArticlePloS one2013
Boolean network model predicts knockout mutant phenotypes of fission yeast.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 45 citations in OpenAlex.
- A system-level model reveals that transcriptional stochasticity is required for hematopoietic stem cell differentiation.NPJ systems biology and applications · 2024Article
- Stochastic Boolean model of normal and aberrant cell cycles in budding yeast.NPJ systems biology and applications · 2024Article
- A continuous-time stochastic Boolean model provides a quantitative description of the budding yeast cell cycle.Scientific reports · 2022Article
- In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model.Biomolecules · 2022Article
- Data Driven Cell Cycle Model to Quantify the Efficacy of Cancer Therapeutics Targeting Specific Cell-Cycle Phases From Flow Cytometry Results.Frontiers in bioinformatics · 2021Article
- Boolean gene regulatory network model of centromere function in Saccharomyces cerevisiae.Journal of biological physics · 2019Article
- Towards perturbation prediction of biological networks using deep learning.Scientific reports · 2019Article
- RMut: R package for a Boolean sensitivity analysis against various types of mutations.PloS one · 2019Article
- Predicting perturbation patterns from the topology of biological networks.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Adding phosphorylation events to the core oscillator driving the cell cycle of fission yeast.PloS one · 2018Article
- Ordinary differential equations and Boolean networks in application to modelling of 6-mercaptopurine metabolism.Royal Society open science · 2017Article
- Heart-Rate Variability-More than Heart Beats?Frontiers in public health · 2017Review
- Boolean Models of Biological Processes Explain Cascade-Like Behavior.Scientific reports · 2016Article
- A Dynamic Gene Regulatory Network Model That Recovers the Cyclic Behavior of Arabidopsis thaliana Cell Cycle.PLoS computational biology · 2015Article
- Convergence behaviour and Control in Non-Linear Biological Networks.Scientific reports · 2015Article
- Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome.PLoS computational biology · 2015Article
- Iron acquisition and oxidative stress response in aspergillus fumigatus.BMC systems biology · 2015Article
- A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks.Scientific reports · 2015Article
- Controllability of time-delayed Boolean multiplex control networks under asynchronous stochastic update.Scientific reports · 2014Article
- Quantitative analysis of intracellular communication and signaling errors in signaling networks.BMC systems biology · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BOOLEAN NETWORKS (OR: networks of switches) are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene regulatory networks in living cells. For example, the temporal sequence of cell cycle activation patterns in yeasts S. pombe and S. cerevisiae are faithfully reproduced by Boolean network models. An interesting question is whether this simple model class could also predict a more complex cellular phenomenology as, for example, the cell cycle dynamics under various knockout mutants instead of the wild type dynamics, only. Here we show that a Boolean network model for the cell cycle control network of yeast S. pombe correctly predicts viability of a large number of known mutants. So far this had been left to the more detailed differential equation models of the biochemical kinetics of the yeast cell cycle network and was commonly thought to be out of reach for models as simplistic as Boolean networks. The new results support our vision that Boolean networks may complement other mathematical models in systems biology to a larger extent than expected so far, and may fill a gap where simplicity of the model and a preference for an overall dynamical blueprint of cellular regulation, instead of biochemical details, are in the focus.
Indexed as
Identifiers
What OpenQuestion holds
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.