Inferring gene regulatory networks from multiple time course gene expression datasets

We proposed a scheme to infer gene regulatory networks from multiple time course gene expression datasets. As the scarcity of time course data, most current methods usually making the inferred gene regulatory network structure as an ill-posed one, and typically cannot handle multiple experimental datasets directly. On the other hand, gene expression data generated by different groups worldwide are increasingly accumulated. In this paper, we first formulate the inference of sparse and stable gene regulatory networks as a constraint optimization problem, which can be easily solved by a given single dataset. Then, two methods of network combination are proposed, which can combine structures inferred from various experimental datasets. After that, the parameters in gene regulatory network with that structure are estimated by solving another optimization problem. Finally, we test and validate our methods on synthetic datasets in a series of numerical experiments in terms of the structure accuracy and the model error.