Ten top reasons for systems biology to get into model-driven engineering.
Résumé
Recent progresses in post-genomic biology allow addressing issues at the system level through the coupling of experimental and modeling approaches that constitute the core of Systems Biology (SB). The challenge for researchers is to integrate disparate data into models to infer useful biological knowledge. Problems which are questioning Model-Driven Engineering (MDE) deal with complexity, systems modeling (including modularity and structure/behavior relationships), models networking and integration, domain-specific language, etc. Among these issues, ten arguments are presented to consider the conceptual convergence and cross-fertilization of SB with MDE. Current models in SB are reviewed and the main requirements to conduct models integration are identified. Some perspectives are provided towards the translation of high-level SB descriptions into formal languages for performing static and dynamic analyses.