ArticleScientific reports2018
Determining Relative Dynamic Stability of Cell States Using Boolean Network Model.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
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- Canalizing kernel for cell fate determination.Briefings in bioinformatics · 2024Article
- Decoding the principle of cell-fate determination for its reverse control.NPJ systems biology and applications · 2024Article
- Article
- Preponderance of generalized chain functions in reconstructed Boolean models of biological networks.Scientific reports · 2024Article
- A neural network-based model framework for cell-fate decisions and development.Communications biology · 2024Article
- Cell reprogramming design by transfer learning of functional transcriptional networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Deep learning untangles the resistance mechanism of p53 reactivator in lung cancer cells.iScience · 2023Article
- Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion.NPJ systems biology and applications · 2023Article
- Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity.iScience · 2023Article
- Emergent Criticality in Coupled Boolean Networks.Entropy (Basel, Switzerland) · 2023Article
- Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics.Frontiers in network physiology · 2023Review
- Probabilistic boolean networks predict transcription factor targets to induce transdifferentiation.iScience · 2022Article
- Comparison of cell state models derived from single-cell RNA sequencing data: graph versus multi-dimensional space.Mathematical biosciences and engineering : MBE · 2022Article
- A preference for link operator functions can drive Boolean biological networks towards critical dynamics.Journal of biosciences · 2022Article
- Review
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Authors and funding
4 authors.
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Abstract
Cell state transition is at the core of biological processes in metazoan, which includes cell differentiation, epithelial-to-mesenchymal transition (EMT) and cell reprogramming. In these cases, it is important to understand the molecular mechanism of cellular stability and how the transitions happen between different cell states, which is controlled by a gene regulatory network (GRN) hard-wired in the genome. Here we use Boolean modeling of GRN to study the cell state transition of EMT and systematically compare four available methods to calculate the cellular stability of three cell states in EMT in both normal and genetically mutated cases. The results produced from four methods generally agree but do not totally agree with each other. We show that distribution of one-degree neighborhood of cell states, which are the nearest states by Hamming distance, causes the difference among the methods. From that, we propose a new method based on one-degree neighborhood, which is the simplest one and agrees with other methods to estimate the cellular stability in all scenarios of our EMT model. This new method will help the researchers in the field of cell differentiation and cell reprogramming to calculate cellular stability using Boolean model, and then rationally design their experimental protocols to manipulate the cell state transition.
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