Inferring Large Scale Genetic Networks with S-System Model

Gene regulatory network (GRN) reconstruction from high-throughput microarray data is an important problem in systems biology. The S-System model, a differential equation based approach, is among the mainstream approaches for modeling GRNs. It has the ability to represent GRNs accurately with precise regulatory weights. However, the current applications of S-System are limited to small and medium scale network, as inferring large network requires inhibitive computational cost. In this paper, we propose a novel S-System based framework to reconstruct biologically relevant GRNs by exploiting their special topological structure. In GRNs, the complex interactions occurring amongst transcription factors (TFs) and target genes (TGs) are unidirectional, i.e., TFs to TGs, and the vice-versa is biologically irrelevant. In addition, TFs can regulate themselves while only self-regulations may exist for TGs. As such, we decompose GRN into two sub-networks representing TF-TF and TF-TG interactions. We learn the sub-networks separately by adapting the traditional S-System model, and combining the solutions to get the entire network. Our experimental studies indicate that the proposed approach can scale up to larger networks, not achievable with other current S-System based approaches, yet with higher accuracy.