An immune network approach to learning qualitative models of biological pathways

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

2 Citations (Scopus)
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In this paper we continue the research on learning qualitative differential equation (QDE) models of biological pathways building on previous work. In particular, we adapt opt-AiNet, an immune-inspired network approach, to effectively search the qualitative model space. To improve the performance of opt-AiNet on the discrete search space, the hypermutation operator has been modified, and the affinity between two antibodies has been redefined. In addition, to accelerate the model verification process, we developed a more efficient Waltz-like inverse model checking algorithm. Finally, a Bayesian scoring function is incorporated into the fitness evaluation to better guide the search. Experimental results on learning the detoxification pathway of Methylglyoxal with various hypothesised hidden species validate the proposed approach, and indicate that our opt-AiNet based approach outperforms the previous CLONALG based approach on qualitative pathway identification.
Original languageEnglish
Title of host publication2014 IEEE Congress on Evolutionary Computation (IEEE CEC 2014)
PublisherIEEE Press
Number of pages8
ISBN (Print)978-1-4799-6626-4
Publication statusPublished - 2014

Bibliographical note

GMC is supported by the CRISP project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative. WP and GMC are also supported by the partnership fund from dot.rural, RCUK Digital Economy research.


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