Structure identification for a gene regulation network by a sparse reconstruction approach

Identifying gene regulation networks (GRNs) which consist of a large number of interacting units, has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from experimental expression data. In this paper, we propose a sparse reconstruction framework to identify GRN structure. Different from traditional methods, the sparse reconstruction approach is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to infer these causal relationships. Efficiency of this method is tested by an artificial linear model and a MAPK pathway model. The performance of suggested approach is compared with the state-of-the-art methods and the widely adopted total least-squares (TLS) method. Computation results show that with a lower computational cost, the proposed method can significantly enhance estimation accuracy and can greatly reduce false positive and negative errors.