ArticleComputational and structural biotechnology journal2022
Identification of dynamic driver sets controlling phenotypical landscapes.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A novel quantum algorithm for efficient attractor search in gene regulatory networks.Patterns (New York, N.Y.) · 2025Article
- Boolean network modeling and its integration with experimental read-outs : An interdisciplinary presentation using a leukemia model.Pathologie (Heidelberg, Germany) · 2024Review
- GatekeepR: an R Shiny application for the identification of nodes with high dynamic impact in Boolean networks.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
10 authors.
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Abstract
Controlling phenotypical landscapes is of vital interest to modern biology. This task becomes highly demanding because cellular decisions involve complex networks engaging in crosstalk interactions. Previous work on control theory indicates that small sets of compounds can control single phenotypes. However, a dynamic approach is missing to determine the drivers of the whole network dynamics. By analyzing 35 biologically motivated Boolean networks, we developed a method to identify small sets of compounds sufficient to decide on the entire phenotypical landscape. These compounds do not strictly prefer highly related compounds and show a smaller impact on the stability of the attractor landscape. The dynamic driver sets include many intervention targets and cellular reprogramming drivers in human networks. Finally, by using a new comprehensive model of colorectal cancer, we provide a complete workflow on how to implement our approach to shift from
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