An improved swarm optimization for parameter estimation and biological model selection
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 incor...
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| Format: | Article |
| Language: | English |
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Plos One
2013
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| Online Access: | http://eprints.utm.my/49048/1/AfnizanfaizalAbdullah2013_Animprovedswarmoptimization.pdf |
| _version_ | 1796859420376825856 |
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| author | Abdullah, Afnizanfaizal Deris, Safaai Mohamad, Mohd. Saberi Anwar, Sohail |
| author_facet | Abdullah, Afnizanfaizal Deris, Safaai Mohamad, Mohd. Saberi Anwar, Sohail |
| author_sort | Abdullah, Afnizanfaizal |
| collection | ePrints |
| description | 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 |
| first_indexed | 2024-03-05T19:26:54Z |
| format | Article |
| id | utm.eprints-49048 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T19:26:54Z |
| publishDate | 2013 |
| publisher | Plos One |
| record_format | dspace |
| spelling | utm.eprints-490482018-10-14T08:21:55Z http://eprints.utm.my/49048/ An improved swarm optimization for parameter estimation and biological model selection Abdullah, Afnizanfaizal Deris, Safaai Mohamad, Mohd. Saberi Anwar, Sohail QA75 Electronic computers. Computer science 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 Plos One 2013 Article PeerReviewed application/pdf en http://eprints.utm.my/49048/1/AfnizanfaizalAbdullah2013_Animprovedswarmoptimization.pdf Abdullah, Afnizanfaizal and Deris, Safaai and Mohamad, Mohd. Saberi and Anwar, Sohail (2013) An improved swarm optimization for parameter estimation and biological model selection. Plos One, 8 (4). ISSN 1932-6203 http://dx.doi.org/10.1371/journal.pone.0061258 DOI: 10.1371/journal.pone.0061258 |
| spellingShingle | QA75 Electronic computers. Computer science Abdullah, Afnizanfaizal Deris, Safaai Mohamad, Mohd. Saberi Anwar, Sohail An improved swarm optimization for parameter estimation and biological model selection |
| title | An improved swarm optimization for parameter estimation and biological model selection |
| title_full | An improved swarm optimization for parameter estimation and biological model selection |
| title_fullStr | An improved swarm optimization for parameter estimation and biological model selection |
| title_full_unstemmed | An improved swarm optimization for parameter estimation and biological model selection |
| title_short | An improved swarm optimization for parameter estimation and biological model selection |
| title_sort | improved swarm optimization for parameter estimation and biological model selection |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/49048/1/AfnizanfaizalAbdullah2013_Animprovedswarmoptimization.pdf |
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