Ali AYADI ICube Laboratory Télécom Physique Strasbourg 300 bd Sébastien Brant - CS 10413 F - 67412 Illkirch cedex
Office: C335 Phone: +33 (0) 6 56 76 34 46 Email: ali.ayadi (at) unistra (dot) fr
Title: Semantic technologies for the optimization of complex biomolecular networks
Promotor: Cecilia Zanni-Merk (Senior Tenured Associate Professor, ICube-SDC and INSA Strasbourg)
Overview: This PhD thesis is prepared as part of a Franco-Tunisian cotutelle between, the SDC Team in collaboration with the LBGI Team and the University of Tunis. The general goal of this thesis is to find an optimal set of external stimuli to be applied during a predetermined time interval to evolve the network from its current state to another desired state. Our original approach is based on the combined use of semantic technologies, combinatorial optimization and simulation.
A complex biomolecular network is represented by the interactions of many molecules (genes, proteins and metabolites) in a cell. This network should stay at a normal (at least, healthy) phenotype. However, by some unknown perturbation or stimuli, the network can be transited from a normal phenotype to a disease phenotype. It thus is desirable to steer the biomolecular network to transit from the abnormal phenotype to a healthy phenotype. Here we are interested in how to effectively steer the system from an unexpected state to a desired state by applying suitable input control signals. The main purpose of this project is to provide a platform based on two strong points of the Data Mining Theme of the SDC team of ICube: semantic technologies on the one hand, and combinatorial optimisation tools on the other hand.
With this aim in mind, our future work will continue to develop a platform to study the transitions of biomolecular networks from any state to a specific state, based on three modules: (i) The ontological module: This module uses semantic technologies to generate new inferred knowledges (the discovery of new semantic associations between molecules) to refine the transitions study of the network behaviour. The input of this module is a set of native data (network states and transitions in the form of values and parameters) introduced by the expert and as a result provides the inferred network composed by native and inferred transition states. This enrichment by metadata and new knowledges will facilitate decision making thanks to a powerful knowledge management. (ii) The simulation module: This module will reproduce over time the dynamic behavior of each network component. This simulator will adopt the DEVS Discrete Event Specification Formalism. (iii) The optimization module: With this module, we apply combinatorial optimization algorithms to provide a set sequences of transitions offering the best control of the network from one state to another, at the same time describing all the changes in values taking place inside each network component.
Teaching assistant at the UFR Mathématiques-Informatique (department of Mathematics and Computer Science) and at the Faculté de Géographie et d'Aménagement (University Institute of Technology) of the University of Strasbourg.
- L1/MathInfo Computer Science S1 :Computer and internet certificate (C2i)
- Master1/GE-OTG Computer Science S2: Spatial databases and SQL (PostgreSQL)
- L1/MathInfo Computer Science S2:Databases and SQL (Oracle)
- L1/MathInfo Computer Science S2: Object-Oriented Programming (Ocaml)