Integrating heterogeneous knowledges for understanding biological behaviors: a probabilistic approach

Jérémie Bourdon (1) , Damien Eveillard (1) , Samuel Gabillard (1) , Theo Merle (1, 2)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique
2 ENS Cachan - École normale supérieure - Cachan

Despite recent molecular technique improvements, biological knowledge remains incomplete. Reasoning on living systems hence implies to integrate heterogeneous and partial informations. Although current investigations successfully focus on qualitative behaviors of macromolecular networks, others approaches show partial quantitative informations like protein concentration variations over times. We consider that both informations, qualitative and quantitative, have to be combined into a modeling method to provide a better understanding of the biological system. We propose here such a method using a probabilistic-like approach. After its exhaustive description, we illustrate its advantages by modeling the carbon starvation response in Escherichia coli. In this purpose, we build an original qualitative model based on available observations. After the formal verification of its qualitative properties, the probabilistic model shows quantitative results corresponding to biological expectations which confirm the interest of our probabilistic approach.

Mots clés: genes regulatory network; weighted Markov chain; hybrid system

Domaines: Bio-informatique [q-bio.QM]; Bio-Informatique, Biologie Systémique [q-bio.QM]

Jérémie Bourdon, Damien Eveillard, Samuel Gabillard, Theo Merle. Integrating heterogeneous knowledges for understanding biological behaviors: a probabilistic approach. 2007. ⟨hal-00199362⟩