Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity

被引:29
|
作者
Noor, Amina [1 ]
Serpedin, Erchin [1 ]
Nounou, Mohamed [2 ]
Nounou, Hazem N. [3 ]
机构
[1] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA
[2] Texas A& M Univ Qatar, Dept Chem Engn, Doha, Qatar
[3] Texas A& M Univ Qatar, Dept Elect Engn, Doha, Qatar
基金
美国国家科学基金会;
关键词
Gene regulatory network; particle filter; Kalman filter; parameter estimation; LASSO; INFERENCE; SIMULATION; SYSTEMS;
D O I
10.1109/TCBB.2012.32
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
This paper considers the problem of learning the structure of gene regulatory networks from gene expression time series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended Kalman filter ( EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and sparse structure.
引用
收藏
页码:1203 / 1211
页数:9
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