Identification of biochemical networks by S-tree based genetic programming

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@Article{Cho:2006:B,

• author = "Dong-Yeon Cho and Kwang-Hyun Cho and Byoung-Tak Zhang",
• title = "Identification of biochemical networks by S-tree based genetic programming",
• journal = "Bioinformatics",
• year = "2006",
• volume = "22",
• number = "13",
• pages = "1631--1640",
• month = jul,
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1367-4803",
• DOI = "doi:10.1093/bioinformatics/btl122",
• abstract = "Motivation: Most previous approaches to model biochemical networks have focused either on the characterisation of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviours of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system. Results: We propose a new representation named S-trees for both the structural and dynamical modelling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are less than 5percent regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within 10percent noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10**2 (??). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia coli. We confirm that the proposed algorithm can successfully identify the true structure except only one relation. Availability: The executable program and data are available from the authors upon request.",
• notes = "C The Author 2006",

}

Genetic Programming entries for Dong-Yeon Cho Kwang-Hyun Cho Byoung-Tak Zhang

Citations