Biologie des systèmes

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.

• Methods: constraint logic programming, symbolic dynamics, machine learning, formal systems.
• Expertise: functional characterization, non-model species, multi-scale integration.
• Application domains: marine biology, micro-environmental biology...

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 focus on a targeted functional characterization rather than on a complete understanding of the species.
• We rely on knowledge rather than on amounts of experimentations.
• We use model-species to validate methods.

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.

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.

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.