Empirical study of computational intelligence strategies for biochemical systems modelling

Zujian Wu, Crina Grosan, David Gilbert

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Citations (Scopus)


Modelling biochemical networks can be achieved by iteratively analyzing parts of the systems via top-down or bottom-up approaches. It is feasible to piece-wise model the biochemical networks from scratch by employing strategies able to assemble reusable components. In this paper, we investigate a set of strategies that can be employed in a bottom-up piece-wise modelling framework, to obtain synthetic models with similar behaviour to the target systems. A combination of evolution strategies and simulated annealing is employed to optimize the structure of the system and its kinetic rates. Simulation results of different variants of those computational methods on a standard signaling pathway show that it is feasible to obtain a tradeoff between the generation of desired behaviour and similar and alternative topologies.

Original languageEnglish
Title of host publicationNature Inspired Cooperative Strategies for Optimization (NICSO 2013)
Subtitle of host publicationLearning, Optimization and Interdisciplinary Applications
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783319016917
Publication statusPublished - 2014

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


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