Sig2GRN : a software tool linking signaling pathway with gene regulatory network for dynamic simulation

• Main Authors: Zhang, Fan, Liu, Runsheng, Zheng, Jie
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2018
• Subjects: Transcription Factor Activity; Gene Regulatory Network; DRNTU::Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/80431; http://hdl.handle.net/10220/46551; https://doi.org/10.21979/N9/SO9VRB
• Institution: Nanyang Technological University

Background: Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. Methods: A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Results: Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. Conclusions: As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design.