ArticlePloS one2013
An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks.Frontiers in genetics · 2019Review
- A modified particle swarm optimization algorithm for parameter estimation of a biological system.Theoretical biology & medical modelling · 2018Article
- Characterization of fibril dynamics on three timescales by solid-state NMR.Journal of biomolecular NMR · 2016Article
- Parameter Estimation Using Divide-and-Conquer Methods for Differential Equation Models.Journal of biometrics & biostatistics · 2016Article
- A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.PloS one · 2014Article
- A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.BioMed research international · 2014Review
- Internal protein dynamics on ps to μs timescales as studied by multi-frequency (15)N solid-state NMR relaxation.Journal of biomolecular NMR · 2013Article
- An improved swarm optimization for parameter estimation and biological model selection.PloS one · 2013Article
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Authors and funding
4 authors.
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Abstract
The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.
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