ArticleNPJ systems biology and applications2017
Reverse engineering highlights potential principles of large gene regulatory network design and learning.
Article in NPJ systems biology and applications, 2017. 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.
- Selecting synthetic data for successful simulation-based transfer learning in dynamical biological systems.BMC bioinformatics · 2026Article
- Gene circuit-based sensors.Fundamental research · 2025Review
- Testing and overcoming the limitations of modular response analysis.Briefings in bioinformatics · 2025Article
- Article
- Modular response analysis reformulated as a multilinear regression problem.Bioinformatics (Oxford, England) · 2023Article
- Cracking the Code of Neuronal Cell Fate.Cells · 2023Review
- NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity.Genome biology · 2022Article
- Article
- Application of modular response analysis to medium- to large-size biological systems.PLoS computational biology · 2022Article
- What is quantitative plant biology?Quantitative plant biology · 2021Review
- Using extremal events to characterize noisy time series.Journal of mathematical biology · 2020Article
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Authors and funding
3 authors.
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Abstract
Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10
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