ArticleBioinformatics (Oxford, England)2016
Optimal design of gene knockout experiments for gene regulatory network inference.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Control-guided refinement of partially specified Boolean networks: applications to RTK signaling.Bioinformatics (Oxford, England) · 2026Article
- Article
- TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks.BMC bioinformatics · 2024Article
- Knowledge of the perturbation design is essential for accurate gene regulatory network inference.Scientific reports · 2022Article
- Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents.Entropy (Basel, Switzerland) · 2022Article
- High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0.Bioinformatics (Oxford, England) · 2022Article
- The single-cell eQTLGen consortium.eLife · 2020Article
- WASABI: a dynamic iterative framework for gene regulatory network inference.BMC bioinformatics · 2019Article
- Optimal parameter identification of synthetic gene networks using harmony search algorithm.PloS one · 2019Article
- A review of active learning approaches to experimental design for uncovering biological networks.PLoS computational biology · 2017Review
- CLIP-GENE: a web service of the condition specific context-laid integrative analysis for gene prioritization in mouse TF knockout experiments.Biology direct · 2016Article
- TRaCE+: Ensemble inference of gene regulatory networks from transcriptional expression profiles of gene knock-out experiments.BMC bioinformatics · 2016Article
- Bayesian Lookahead Perturbation Policy for Inference of Regulatory Networks.IEEE/ACM transactions on computational biology and bioinformaticsArticle
- Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks.Proceedings of the ... American Control Conference. American Control ConferenceArticle
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2 authors.
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Abstract
motivationWe addressed the problem of inferring gene regulatory network (GRN) from gene expression data of knockout (KO) experiments. This inference is known to be underdetermined and the GRN is not identifiable from data. Past studies have shown that suboptimal design of experiments (DOE) contributes significantly to the identifiability issue of biological networks, including GRNs. However, optimizing DOE has received much less attention than developing methods for GRN inference.
resultsWe developed REDuction of UnCertain Edges (REDUCE) algorithm for finding the optimal gene KO experiment for inferring directed graphs (digraphs) of GRNs. REDUCE employed ensemble inference to define uncertain gene interactions that could not be verified by prior data. The optimal experiment corresponds to the maximum number of uncertain interactions that could be verified by the resulting data. For this purpose, we introduced the concept of edge separatoid which gave a list of nodes (genes) that upon their removal would allow the verification of a particular gene interaction. Finally, we proposed a procedure that iterates over performing KO experiments, ensemble update and optimal DOE. The case studies including the inference of Escherichia coli GRN and DREAM 4 100-gene GRNs, demonstrated the efficacy of the iterative GRN inference. In comparison to systematic KOs, REDUCE could provide much higher information return per gene KO experiment and consequently more accurate GRN estimates.
conclusionsREDUCE represents an enabling tool for tackling the underdetermined GRN inference. Along with advances in gene deletion and automation technology, the iterative procedure brings an efficient and fully automated GRN inference closer to reality. AVAILABILITY AND IMPLEMENTATION: MATLAB and Python scripts of REDUCE are available on www.cabsel.ethz.ch/tools/REDUCE CONTACT: rudi.gunawan@chem.ethz.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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