13385 Marseille Cedex 05
Campus de Luminy,
DIMNP - UMR 5235 CNRS/UM1/UM2
Pl. E. Bataillon - Bat. 24
34095 Montpellier Cedex 5
UMR 1388 INRA-INPT GenPhySE
Génétique, Physiologie et Systèmes d'Elevage
24, Chemin de Borde Rouge CS 52627
31326 Castanet Tolosan Cedex
IRISA & INRIA Rennes
Campus de Beaulieu
35042 Rennes cedex
Dyliss is a research team in bioinformatics. We focus on sequence analysis and systems biology. We use qualitative formal systems to characterize genetic actors from non model species, such as algae or mining baceria, that control phenotypic answers when challenged by their environment.
The main computational challenge is to face lacks and incompleteness in both expert knowledge and experimental observations. Our strategy relies on three main points.
We use qualitative formal systems for knowledge acquisition and integration. All our methods aim at identifying the space of all models that are consistent with both knowledge and observations. Then we provide tools to navigate in this space in order to investigate which properties are shared by a large proportion of the space.
75230 PARIS CEDEX 05
Laboratoire des Sciences du Numérique de Nantes (LS2N CNRS UMR6004)
2, rue de la Houssinière
The ComBi team at LINA aims at developping original algorithmic and mathematics methods for studying problems issued from biology. Its main research topics focus on comparative genomics and systems biology.
Laboratory of Biology and Modeling of the Cell
Ecole Normale Supérieure de LYON
Site Jacques Monod, Bâtiment LR3.
46 allée d’Italie -
69354 LYON cedex 07
The molecular mechanisms controlling decision making at the cellular level between self-renewal and differentiation are still poorly understood. The central question of our group consists in understanding the molecular mechanisms controlling self-renewal and the alteration of these mechanisms in relation to the onset of cancer.
For this we abide by the following view : "The stem-cell signature should therefore be determined by systems-biology tools that can identify patterns, rather than by the analysis of individual genes or even multiple gene-product behaviours." Zipori, D. (2004) The nature of stem cells : state rather than entity. Nat. Rev. Genet., 5, 873-878.