ArticleComputational and structural biotechnology journal2021
Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Modeling of gene regulatory networks: an annotated glossary.Trends in plant science · 2026Review
- Unveiling toxicological adverse outcomes: toward construction and simulation of large-scale networks.Toxicological sciences : an official journal of the Society of Toxicology · 2026Review
- Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming.NPJ systems biology and applications · 2025Article
- Control of Cellular Differentiation Trajectories for Cancer Reversion.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Boolean network modeling and its integration with experimental read-outs : An interdisciplinary presentation using a leukemia model.Pathologie (Heidelberg, Germany) · 2024Review
- scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.PLoS computational biology · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective.NPJ systems biology and applications · 2023Article
- Leveraging quantum computing for dynamic analyses of logical networks in systems biology.Patterns (New York, N.Y.) · 2023Article
- A novel Boolean network inference strategy to model early hematopoiesis aging.Computational and structural biotechnology journal · 2023Article
- Modelling speciation: Problems and implications.In silico biology · 2023Article
- CANTATA-prediction of missing links in Boolean networks using genetic programming.Bioinformatics (Oxford, England) · 2022Article
- Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging.Cells · 2022Review
- Executable models of immune signaling pathways in HIV-associated atherosclerosis.NPJ systems biology and applications · 2022Article
- Boolean implication analysis of single-cell data predicts retinal cell type markers.BMC bioinformatics · 2022Article
- Review and assessment of Boolean approaches for inference of gene regulatory networks.Heliyon · 2022Review
- Attractor-Specific and Common Expression Values in Random Boolean Network Models (with a Preliminary Look at Single-Cell Data).Entropy (Basel, Switzerland) · 2022Article
- Systems approaches to investigate the role of NF-κB signaling in aging.The Biochemical journal · 2022Review
- Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?Current issues in molecular biology · 2022Review
- Identification of dynamic driver sets controlling phenotypical landscapes.Computational and structural biotechnology journal · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Regulatory dependencies in molecular networks are the basis of dynamic behaviors affecting the phenotypical landscape. With the advance of high throughput technologies, the detail of omics data has arrived at the single-cell level. Nevertheless, new strategies are required to reconstruct regulatory networks based on populations of single-cell data. Here, we present a new approach to generate populations of gene regulatory networks from single-cell RNA-sequencing (scRNA-seq) data. Our approach exploits the heterogeneity of single-cell populations to generate pseudo-timepoints. This allows for the first time to uncouple network reconstruction from a direct dependency on time series measurements. The generated time series are then fed to a combined reconstruction algorithm. The latter allows a fast and efficient reconstruction of ensembles of gene regulatory networks. Since this approach does not require knowledge on time-related trajectories, it allows us to model heterogeneous processes such as aging. Applying the approach to the aging-associated NF-
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