Qualitative Dynamical Modelling Can Formally Explain Mesoderm Specification and Predict Novel Developmental Phenotypes

Abstract : Given the complexity of developmental networks, it is often difficult to predict the effect of genetic perturbations, even within coding genes. Regulatory factors generally have pleiotropic effects, exhibit partially redundant roles, and regulate highly interconnected pathways with ample cross-talk. Here, we delineate a logical model encompassing 48 components and 82 regulatory interactions involved in mesoderm specification during Drosophila development, thereby providing a formal integration of all available genetic information from the literature. The four main tissues derived from mesoderm correspond to alternative stable states. We demonstrate that the model can predict known mutant phenotypes and use it to systematically predict the effects of over 300 new, often non-intuitive, loss- and gain-of-function mutations, and combinations thereof. We further validated several novel predictions experimentally, thereby demonstrating the robustness of model. Logical modelling can thus contribute to formally explain and predict regulatory outcomes underlying cell fate decisions.
Type de document :
Article dans une revue
PLoS Computational Biology, Public Library of Science, 2016, 12 (9), pp.e1005073. 〈10.1371/journal.pcbi.1005073〉
Liste complète des métadonnées

Littérature citée [24 références]  Voir  Masquer  Télécharger

https://hal-amu.archives-ouvertes.fr/hal-01619081
Contributeur : Lionel Spinelli <>
Soumis le : lundi 17 septembre 2018 - 10:50:23
Dernière modification le : mardi 25 septembre 2018 - 01:10:18

Fichier

journal.pcbi.1005073.PDF
Publication financée par une institution

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Collections

Citation

Abibatou Mbodj, E. Hilary Gustafson, Lucia Ciglar, Guillaume Junion, A. Gonzalez, et al.. Qualitative Dynamical Modelling Can Formally Explain Mesoderm Specification and Predict Novel Developmental Phenotypes. PLoS Computational Biology, Public Library of Science, 2016, 12 (9), pp.e1005073. 〈10.1371/journal.pcbi.1005073〉. 〈hal-01619081〉

Partager

Métriques

Consultations de la notice

51

Téléchargements de fichiers

3