Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, Saskatchewan S7N 5A9, Canada.
IEEE/ACM Trans Comput Biol Bioinform. 2012 Jul-Aug;9(4):955-65. doi: 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.
从实验时间序列数据重建生物系统是系统生物学中的一项具有挑战性的任务。S 系统由一组非线性常微分方程 (ODE) 组成，是描述分子生物学系统和分析系统动力学的有效模型。然而，由于其非线性和复杂性，在不知道系统结构的情况下推断 S 系统并不是一项简单的任务。本文提出了一种用于推断 S 系统的剪枝可分离参数估计算法 (PSPEA)。该新算法结合了可分离参数估计方法 (SPEM) 和剪枝策略，其中包括向目标函数添加 l₁正则化项，并使用阈值修剪解。然后，该算法与连续遗传算法 (CGA) 相结合，形成一种具有这两种组合算法特性的混合算法。从参数估计误差和结构识别准确性两个方面评估了所提出算法中剪枝策略的性能。结果表明，与现有方法相比，具有剪枝策略的所提出算法具有更低的估计误差和更高的识别准确性。
