Biochemical systems identification by a random drift particle swarm optimization approach

Jun Sun, Vasile Palade, Yujie Cai, Wei Fang, Xiaojun Wu

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    17 Citations (Scopus)
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    Background: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. Results: This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. Conclusions: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study.
    Original languageEnglish
    Article numberS1
    JournalBMC Bioinformatics
    Issue numberSuppl 6
    Publication statusPublished - 16 May 2014

    Bibliographical note

    © 2014 Sun et al.; licensee BioMed Central Ltd. This is an Open Access article distributed
    under the terms of the Creative Commons Attribution License
    (, which permits unrestricted use, distribution,
    and reproduction in any medium, provided the original work is properly cited. The Creative
    Commons Public Domain Dedication waiver
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    this article, unless otherwise stated.


    This work is supported by the Natural Science Foundation of Jiangsu Province, China (Project Number: BK2010143), by the Natural Science Foundation of China (Project Numbers 61170119, 61105128, 61373055), by the Program for New Century Excellent Talents in University (Project Number: NCET-11-0660), by the RS-NSFC International Exchange Programme (Project Number: 61311130141), and by the Key grant Project of Chinese Ministry of Education (Project Number: 311024).


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