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

Area of Science:

• Computational systems biology
• Bioinformatics
• Molecular systems biology

Background:

• Linking computational models of signaling pathways to cellular responses like gene expression is challenging.
• Sig2GRN is a Cytoscape plugin designed to simulate time-course gene expression data based on user-defined stimuli to signaling pathways.

Purpose of the Study:

• To present Sig2GRN, a novel Cytoscape plugin for simulating gene expression dynamics.
• To bridge the gap between signaling pathway models and predicted cellular responses.

Main Methods:

• Utilizes a generalized logical model for upstream signaling pathways.
• Employs Boolean and thermodynamics-based models for downstream gene expression prediction.
• Simulates gene expression changes based on transcription factor dynamics.

Main Results:

• Sig2GRN successfully simulates time-course gene expression data.
• Case studies demonstrate prediction of gene expression changes induced by DNA damage and drug treatments.
• The software links signaling pathway dynamics to gene expression outcomes.

Conclusions:

• Sig2GRN serves as a valuable software tool for modeling cellular dynamics.
• Facilitates hypothesis generation in systems biology.
• Aids in the design of wet-lab experiments by providing predictive simulations.