An immune-inspired approach to qualitative system identification of biological pathways

摘要: In this paper, a special-purpose qualitative model learning (QML) system using an immune-inspired algorithm is proposed to qualitatively reconstruct biological pathways. We choose real-world application, the detoxification pathway of Methylglyoxal (MG), as case study. First converter implemented convert possible pathways models. Then general strategy presented. To improve scalability QML and make it adapt future more complicated pathways, modified clonal selection (CLONALG) employed search strategy. The performance approach compared with those exhaustive two backtracking algorithms. experimental results indicate that can significantly efficiency when dealing some large-scale spaces.