Monteiro C.B.,University of Porto |
Monteiro C.B.,IBMC Institute Biologia Celular e Molecular |
Costa M.F.,University of Porto |
Costa M.F.,IBMC Institute Biologia Celular e Molecular |
And 7 more authors.
FEBS Letters | Year: 2014
The homeodomain factor paired related homeobox protein-like 1 (Prrxl1) is crucial for proper assembly of dorsal root ganglia (DRG)-dorsal spinal cord (SC) pain-sensing circuit. By performing chromatin immunoprecipitation with either embryonic DRG or dorsal SC, we identified two evolutionarily conserved regions (i.e. proximal promoter and intron 4) of Prrxl1 locus that show tissue-specific binding of Prrxl1. Transcriptional assays confirm the identified regions can mediate repression by Prrxl1, while gain-of-function studies in Prrxl1 expressing ND7/23 cells indicate Prrxl1 can down-regulate its own expression. Altogether, our results suggest that Prrxl1 uses distinct regulatory regions to repress its own expression in DRG and dorsal SC. © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
Corblin F.,CNRS Complex Medical Engineering Laboratory |
Fanchon E.,CNRS Complex Medical Engineering Laboratory |
Trilling L.,CNRS Complex Medical Engineering Laboratory |
Chaouiya C.,Igc Instituto Gulbenkian Of Ciencia |
Thieffry D.,French Institute of Health and Medical Research
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012
Advanced mathematical methods and computational tools are required to properly understand the behavior of large and complex regulatory networks that control cellular processes. Since available data are predominantly qualitative or semi-quantitative, discrete (logical) modeling approaches are increasingly used to model these networks. Here, relying on the multilevel logical formalism developed by R. Thomas et al. [7,9,8], we propose a computational approach enabling (i) to check the existence of at least one consistent model, given partial data on the regulatory structure and dynamical properties, and (ii) to infer properties common to all consistent models. Such properties represent non trivial deductions and could be used by the biologist to design new experiments. Rather than focusing on a single plausible solution, i.e. a model fully defined, we consider the whole class of models consistent with the available data and some economy criteria, from which we deduce shared properties. We use constraint programming to represent this class of models as the set of all solutions of a set of constraints . For the sake of efficiency, we have developed a framework, called SysBiOX, enabling (i) the integration of partial gene interaction and expression data into constraints and (ii) the resolution of these constraints in order to infer properties about the structure or the behaviors of the gene network. SysBiOX is implemented in ASP (Answer Set Programming) using Clingo . © 2012 Springer-Verlag.