Molecular mechanisms of system responses to novel stimuli are predictable from public data

Samuel A. Danziger, Alexander V. Ratushny, Jennifer J. Smith, Ramsey A. Saleem, Yakun Wan, Christina E. Arens, Abraham M. Armstrong, Katherine Sitko, Wei Ming Chen, Jung Hsien Chiang, David J. Reiss, Nitin S. Baliga, John D. Aitchison

研究成果: Article同行評審

19 引文 斯高帕斯(Scopus)

摘要

Systems scale models provide the foundation for an effective iterative cycle between hypothesis generation, experiment and model refinement. Such models also enable predictions facilitating the understanding of biological complexity and the control of biological systems. Here, we demonstrate the reconstruction of a globally predictive gene regulatory model from public data: a model that can drive rational experiment design and reveal new regulatory mechanisms underlying responses to novel environments. Specifically, using -1500 publically available genome-wide transcriptome data sets from Saccharomyces cerevisiae, we have reconstructed an environment and gene regulatory influence network that accurately predicts regulatory mechanisms and gene expression changes on exposure of cells to completely novel environments. Focusing on transcriptional networks that induce peroxisomes biogenesis, the model-guided experiments allow us to expand a core regulatory network to include novel transcriptional influences and linkage across signaling and transcription. Thus, the approach and model provides a multi-scalar picture of gene dynamics and are powerful resources for exploiting extant data to rationally guide experimentation. The techniques outlined here are generally applicable to any biological system, which is especially important when experimental systems are challenging and samples are difficult and expensive to obtain-a common problem in laboratory animal and human studies.

原文English
頁(從 - 到)1442-1460
頁數19
期刊Nucleic acids research
42
發行號3
DOIs
出版狀態Published - 2014 二月

All Science Journal Classification (ASJC) codes

  • Genetics

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