CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2012 vol.9 Issue No.04 - July-Aug.
Issue No.04 - July-Aug. (2012 vol.9)
Fang-Xiang Wu , Dept. of Mech. Eng., Univ. of Saskatchewan, Saskatoon, SK, Canada
Li-Zhi Liu , Dept. of Mech. Eng., Univ. of Saskatchewan, Saskatoon, SK, Canada
W. J. Zhang , Dept. of Mech. Eng., Univ. of Saskatchewan, Saskatoon, SK, Canada
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2011.126
Reconstruction of a biological system from its experimental time series data is a challenging task in systems biology. The S-system which consists of a group of nonlinear ordinary differential equations (ODEs) is an effective model to characterize molecular biological systems and analyze the system dynamics. However, inference of S-systems without the knowledge of system structure is not a trivial task due to its nonlinearity and complexity. In this paper, a pruning separable parameter estimation algorithm (PSPEA) is proposed for inferring S-systems. This novel algorithm combines the separable parameter estimation method (SPEM) and a pruning strategy, which includes adding an ℓ1 regularization term to the objective function and pruning the solution with a threshold value. Then, this algorithm is combined with the continuous genetic algorithm (CGA) to form a hybrid algorithm that owns the properties of these two combined algorithms. The performance of the pruning strategy in the proposed algorithm is evaluated from two aspects: the parameter estimation error and structure identification accuracy. The results show that the proposed algorithm with the pruning strategy has much lower estimation error and much higher identification accuracy than the existing method.
time series, bioinformatics, genetic algorithms, molecular biophysics, nonlinear differential equations, parameter estimation, structure identification accuracy, biological S-system, separable estimation method, biological system reconstruction, time series data, systems biology, nonlinear ordinary differential equations, ODE, molecular biological systems, system dynamics, system structure, pruning separable parameter estimation algorithm, separable parameter estimation method, pruning strategy, ℓ1 regularization term, continuous genetic algorithm, parameter estimation error, Kinetic theory, Parameter estimation, Mathematical model, Biological system modeling, Biological systems, Time series analysis, Genetic algorithms, genetic algorithm., Separable parameter estimation, structure identification, biological systems, S-system, \ell_1 regularization
Fang-Xiang Wu, Li-Zhi Liu, W. J. Zhang, "Inference of Biological S-System Using the Separable Estimation Method and the Genetic Algorithm", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 4, pp. 955-965, July-Aug. 2012, doi:10.1109/TCBB.2011.126