Gene Regulatory Network modelling: a state-space approach

This study proposes a state-space model with control portion for inferring Gene Regulatory Networks (GRNs). The proposed model views genes as the observation variables, whose expression values depend on the current internal state variables and control variables, and views the means of clusters of gene expression as the control variables of the internal state equation. Bayesian Information Criterion (BIC) and Probabilistic Principal Component Analysis (PPCA) are used to estimate the internal states from observation data. The proposed approach is applied to two gene expression datasets. Computational results show that inferred GRNs possesses the characteristics of the real-life GRNs.