Linking the signaling cascades and dynamic regulatory networks controlling stress responses

• Published in: Genome research 2013-02, Vol.23 (2), p.365-376
• Main Authors: Gitter, Anthony, Carmi, Miri, Barkai, Naama, Bar-Joseph, Ziv
• Format: Article
• Language: English
• Subjects: Algorithms; Arabidopsis - genetics; Arabidopsis - immunology; Arabidopsis - metabolism; Gene Expression Regulation - drug effects; Gene Knockout Techniques; Gene Regulatory Networks - drug effects; Method; Models, Biological; Osmolar Concentration; Reproducibility of Results; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Signal Transduction - drug effects; Sirolimus - pharmacology; Stress, Physiological - drug effects; Stress, Physiological - physiology; Time Factors
• ISSN: 1088-9051; 1549-5469
• DOI: 10.1101/gr.138628.112

Accurate models of the cross-talk between signaling pathways and transcriptional regulatory networks within cells are essential to understand complex response programs. We present a new computational method that combines condition-specific time-series expression data with general protein interaction data to reconstruct dynamic and causal stress response networks. These networks characterize the pathways involved in the response, their time of activation, and the affected genes. The signaling and regulatory components of our networks are linked via a set of common transcription factors that serve as targets in the signaling network and as regulators of the transcriptional response network. Detailed case studies of stress responses in budding yeast demonstrate the predictive power of our method. Our method correctly identifies the core signaling proteins and transcription factors of the response programs. It further predicts the involvement of additional transcription factors and other proteins not previously implicated in the response pathways. We experimentally verify several of these predictions for the osmotic stress response network. Our approach requires little condition-specific data: only a partial set of upstream initiators and time-series gene expression data, which are readily available for many conditions and species. Consequently, our method is widely applicable and can be used to derive accurate, dynamic response models in several species.