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

Wei Pang, George M. Coghill

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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 a real-world application, the detoxification pathway of Methylglyoxal (MG), as a case study. First a converter is implemented to convert possible pathways to qualitative models. Then a general learning strategy is presented. To improve the scalability of the proposed QML system and make it adapt to future more complicated pathways, a modified clonal selection algorithm (CLONALG) is employed as the search strategy. The perfor- mance of this immune-inspired approach is compared with those of exhaustive search and two backtracking algorithms. The experimental results indicate that this immune-inspired approach can significantly improve the search efficiency when dealing with some complicated pathways with large-scale search spaces.
Original languageEnglish
Pages (from-to)189-207
Number of pages19
JournalNatural Computing
Issue number1
Publication statusPublished - Mar 2011


  • clonal selection algorithm
  • immune-inspired algorithm
  • pathway reconstruction
  • qualitative differential equation
  • qualitative model learning
  • qualitative reasoning
  • qualitative simulation


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