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TopoDoE: a design of experiment strategy for selection and refinement in ensembles of executable gene regulatory networks.
Bouvier, Matteo; Zreika, Souad; Vallin, Elodie; Fourneaux, Camille; Gonin-Giraud, Sandrine; Bonnaffoux, Arnaud; Gandrillon, Olivier.
Afiliación
  • Bouvier M; Laboratoire de Biologie Moléculaire de la Cellule, Lyon, France. matteo.bouvier@ens-lyon.fr.
  • Zreika S; Vidium Solutions, Lyon, France. matteo.bouvier@ens-lyon.fr.
  • Vallin E; Inria Grenoble, Rhône-Alpes Research Center, Lyon, France. matteo.bouvier@ens-lyon.fr.
  • Fourneaux C; Laboratoire de Biologie Moléculaire de la Cellule, Lyon, France.
  • Gonin-Giraud S; Laboratoire de Biologie Moléculaire de la Cellule, Lyon, France.
  • Bonnaffoux A; Laboratoire de Biologie Moléculaire de la Cellule, Lyon, France.
  • Gandrillon O; Laboratoire de Biologie Moléculaire de la Cellule, Lyon, France.
BMC Bioinformatics ; 25(1): 245, 2024 Jul 19.
Article en En | MEDLINE | ID: mdl-39030497
ABSTRACT

BACKGROUND:

Inference of Gene Regulatory Networks (GRNs) is a difficult and long-standing question in Systems Biology. Numerous approaches have been proposed with the latest methods exploring the richness of single-cell data. One of the current difficulties lies in the fact that many methods of GRN inference do not result in one proposed GRN but in a collection of plausible networks that need to be further refined. In this work, we present a Design of Experiment strategy to use as a second stage after the inference process. It is specifically fitted for identifying the next most informative experiment to perform for deciding between multiple network topologies, in the case where proposed GRNs are executable models. This strategy first performs a topological analysis to reduce the number of perturbations that need to be tested, then predicts the outcome of the retained perturbations by simulation of the GRNs and finally compares predictions with novel experimental data.

RESULTS:

We apply this method to the results of our divide-and-conquer algorithm called WASABI, adapt its gene expression model to produce perturbations and compare our predictions with experimental results. We show that our networks were able to produce in silico predictions on the outcome of a gene knock-out, which were qualitatively validated for 48 out of 49 genes. Finally, we eliminate as many as two thirds of the candidate networks for which we could identify an incorrect topology, thus greatly improving the accuracy of our predictions.

CONCLUSION:

These results both confirm the inference accuracy of WASABI and show how executable gene expression models can be leveraged to further refine the topology of inferred GRNs. We hope this strategy will help systems biologists further explore their data and encourage the development of more executable GRN models.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Redes Reguladoras de Genes Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Francia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Redes Reguladoras de Genes Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Francia