title: Formal methods for Hopfield-like networks.
abstract: Building a meaningful model of biological regulatory network is usually done by specifying the components (e.g. the genes) and their interactions, by guessing the values of parameters, by comparing the predicted behaviors to the observed ones, and by modifying in a trial-error process both architecture and parameters in order to reach an optimal fitness. We propose here a different approach to construct and analyze biological models avoiding the trial-error part, where structure and dynamics are represented as formal constraints. We apply the method to Hopfield-like networks, a formalism often used in both neural and regulatory networks modeling. The aim is to characterize automatically the set of all models consistent with all the available knowledge (about structure and behavior). The available knowledge is formalized into formal constraints. The latter are compiled into Boolean formula in conjunctive normal form and then submitted to a Boolean satisfiability solver. This approach allows to formulate a wide range of queries, expressed in a high level language, and possibly integrating formalized intuitions. In order to explore its potential, we use it to find cycles for 3-nodes networks and to determine the flower morphogenesis regulatory network of Arabidopsis thaliana. Applications of this technique are numerous and concern the building of models from data as well as the design of biological networks possessing specified behaviors.
authors: UJF-University of Grenoble 1-CNRS, AGIM Laboratory, Laboratory of Ageing Imaging and Modeling, FRE 3405, Domaine de la Merci, 38700 La Tronche, France.
authors: Hedi
authors: H
authors: Ben Amor
authors: Fabien
authors: F
authors: Corblin
authors: Eric
authors: E
authors: Fanchon
authors: Adrien
authors: A
authors: Elena
authors: Laurent
authors: L
authors: Trilling
authors: Jacques
authors: J
authors: Demongeot
authors: Nicolas
authors: N
authors: Glade
mesh_terms: D016247:Information Storage and Retrieval; D008962:Models, Theoretical; D049490:Systems Biology