title: Data-driven modelling of a gene regulatory network for cell fate decisions in the growing limb bud.
abstract: Parameter optimization coupled with model selection is a convenient approach to infer gene regulatory networks from experimental gene expression data, but so far it has been limited to single cells or static tissues where growth is not significant. Here, we present a computational study in which we determine an optimal gene regulatory network from the spatiotemporal dynamics of gene expression patterns in a complex 2D growing tissue (non-isotropic and heterogeneous growth rates). We use this method to predict the regulatory mechanisms that underlie proximodistal (PD) patterning of the developing limb bud. First, we map the expression patterns of the PD markers Meis1, Hoxa11 and Hoxa13 into a dynamic description of the tissue movements that drive limb morphogenesis. Secondly, we use reverse-engineering to test how different gene regulatory networks can interpret the opposing gradients of fibroblast growth factors (FGF) and retinoic acid (RA) to pattern the PD markers. Finally, we validate and extend the best model against various previously published manipulative experiments, including exogenous application of RA, surgical removal of the FGF source and genetic ectopic expression of Meis1. Our approach identifies the most parsimonious gene regulatory network that can correctly pattern the PD markers downstream of FGF and RA. This network reveals a new model of PD regulation which we call the "crossover model", because the proximal morphogen (RA) controls the distal boundary of Hoxa11, while conversely the distal morphogens (FGFs) control the proximal boundary.
authors.affiliation: EMBL-CRG Systems Biology Program Centre for Genomic Regulation (CRG) Universitat Pompeu Fabra (UPF), Barcelona Spain.
authors.forename: Manu
authors.initials: M
authors.lastname: Uzkudun
authors.affiliation: EMBL-CRG Systems Biology Program Centre for Genomic Regulation (CRG) Universitat Pompeu Fabra (UPF), Barcelona Spain.
authors.forename: Luciano
authors.initials: L
authors.lastname: Marcon
authors.affiliation: EMBL-CRG Systems Biology Program Centre for Genomic Regulation (CRG) Universitat Pompeu Fabra (UPF), Barcelona Spain Institucio Catalana de Recerca i Estudis Avancats (ICREA), Barcelona, Spain james.sharpe@crg.eu.
authors.forename: James
authors.initials: J
authors.lastname: Sharpe
mesh_terms: D000818:Animals; D019521:Body Patterning; D003198:Computer Simulation; D005346:Fibroblast Growth Factors / Q000378:metabolism; D053263:Gene Regulatory Networks; D018398:Homeodomain Proteins / Q000235:genetics*; D018878:Limb Buds / Q000254:growth & development* / Q000378:metabolism; D051379:Mice; D008957:Models, Genetic; D000075064:Myeloid Ecotropic Viral Integration Site 1 Protein; D009363:Neoplasm Proteins / Q000235:genetics*; D046509:Tissue Culture Techniques; D059467:Transcriptome; D014212:Tretinoin / Q000378:metabolism
chemical_list: D018398:Homeodomain Proteins; C094041:Hoxa11 protein, mouse; C000616392:Meis1 protein, mouse; D000075064:Myeloid Ecotropic Viral Integration Site 1 Protein; D009363:Neoplasm Proteins; C099959:homeobox protein HOXA13; D014212:Tretinoin; D005346:Fibroblast Growth Factors
keywords: gene regulatory networks; limb development; morphogen; patterning; reverse‐engineering