Structure identification and parameter estimation of biological s-systems

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 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 is proposed for inferring S-systems. This novel algorithm combines the separable parameter estimation method and a pruning strategy, which includes adding an ℓ₁ regularization term to the objective function and pruning the solution with a threshold value. The performance of the pruning strategy in the proposed algorithm is evaluated from two aspects: the parameter estimation error and structure identification accuracy. The proposed algorithm is applied to two S-systems with simulated data. The results show that the proposed algorithm has much lower estimation error and much higher identification accuracy than the existing method.