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An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

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  • Afnizanfaizal Abdullah
  • Safaai Deris
  • Mohd Saberi Mohamad
  • Sohail Anwar

Abstract

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

Suggested Citation

  • Afnizanfaizal Abdullah & Safaai Deris & Mohd Saberi Mohamad & Sohail Anwar, 2013. "An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-16, April.
  • Handle: RePEc:plo:pone00:0061258
    DOI: 10.1371/journal.pone.0061258
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    References listed on IDEAS

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    1. James O Lloyd-Smith, 2007. "Maximum Likelihood Estimation of the Negative Binomial Dispersion Parameter for Highly Overdispersed Data, with Applications to Infectious Diseases," PLOS ONE, Public Library of Science, vol. 2(2), pages 1-8, February.
    2. Hongyu Miao & Carrie Dykes & Lisa M. Demeter & Hulin Wu, 2009. "Differential Equation Modeling of HIV Viral Fitness Experiments: Model Identification, Model Selection, and Multimodel Inference," Biometrics, The International Biometric Society, vol. 65(1), pages 292-300, March.
    3. Nuno F Lages & Carlos Cordeiro & Marta Sousa Silva & Ana Ponces Freire & António E N Ferreira, 2012. "Optimization of Time-Course Experiments for Kinetic Model Discrimination," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
    4. Afnizanfaizal Abdullah & Safaai Deris & Sohail Anwar & Satya N V Arjunan, 2013. "An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-16, March.
    5. Gabriele Lillacci & Mustafa Khammash, 2010. "Parameter Estimation and Model Selection in Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-17, March.
    6. Xiaodian Sun & Li Jin & Momiao Xiong, 2008. "Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks," PLOS ONE, Public Library of Science, vol. 3(11), pages 1-13, November.
    7. Diego Fernández Slezak & Cecilia Suárez & Guillermo A Cecchi & Guillermo Marshall & Gustavo Stolovitzky, 2010. "When the Optimal Is Not the Best: Parameter Estimation in Complex Biological Models," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-10, October.
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