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Natural Computing

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01.03.2011

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

verfasst von: Wei Pang, George M. Coghill

Erschienen in: Natural Computing | Ausgabe 1/2011

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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.

Vorheriger Artikel Editorial for special issue on the interaction between computation and biology

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This quantitative model has not been published yet. So it will not be presented in this paper.

Data obtained by Prof. Ian Booth’s research group at Aberdeen University.

Note that Eq. 5 is an arbitrary example given, and it is not relevant to the MG model.

Note we use the word “may” is because CLONALG is a randomised algorithm, although several successful experiments performed in previous work showed that CLONALG did improve the scalability of QML when learning these particular problems, it is not completely safe to say that CLONALG can improve the scalability of QML for learning any other problems in general. In addition, even for the previous successful experiments, when conditions and configurations of the problems are changed, we might get negative results. In summary, we cannot guarantee that CLONALG always (statistically) performs better than the deterministic algorithms in any conditions for any problem.

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Titel: An immune-inspired approach to qualitative system identification of biological pathways
verfasst von: Wei Pang
George M. Coghill
Publikationsdatum: 01.03.2011
Verlag: Springer Netherlands
Erschienen in: Natural Computing / Ausgabe 1/2011
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI: https://doi.org/10.1007/s11047-010-9212-2

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