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

Authors:
Wei Pang;George M. Coghill
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China 130012 and Department of Computing Science, University of Aberdeen, Aberdeen, UK AB24 3UE;Department of Computing Science, University of Aberdeen, Aberdeen, UK AB24 3UE
Venue:
Natural Computing: an international journal
Year:
2011

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Abstract

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 performance 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.