西亚试剂：A Predictive Model for Transcriptional Control of Physiolog

发布时间：2026-03-01

A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell

Richard Bonneau,2,5,7 Marc T. Facciotti,1 David J. Reiss,1 Amy K. Schmid,1 Min Pan,1 Amardeep Kaur,1 Vesteinn Thorsson,1 Paul Shannon,1 Michael H. Johnson,1 J. Christopher Bare,1 William Longabaugh,1 Madhavi Vuthoori,1 Kenia Whitehead,1 Aviv Madar,2 Lena Suzuki,4 Tetsuya Mori,4 Dong-Eun Chang,4 Jocelyne DiRuggiero,3 Carl H. Johnson,4 Leroy Hood,1 and Nitin S. Baliga1,6,7,

1 Institute for Systems Biology, Seattle, WA 98103, USA
2 Center for Genomics & Systems Biology, New York University, New York, NY 10003, USA
3 University of Maryland, College Park, MD 20742, USA
4 Vanderbilt University, Nashville, TN 37240, USA
5 Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York, NY 10003, USA
6 Departments of Microbiology and Molecular and Cellular Biology, University of Washington, Seattle, WA 98195, USA

Corresponding author
Nitin S. Baliga
The environment significantly influences the dynamic expression and assembly of all components encoded in the genome of an organism into functional biological networks. We have constructed a model for this process in Halobacterium salinarum NRC-1 through the data-driven discovery of regulatory and functional interrelationships among ∼80% of its genes and key abiotic factors in its hypersaline environment. Using relative changes in 72 transcription factors and 9 environmental factors (EFs) this model accurately predicts dynamic transcriptional responses of all these genes in 147 newly collected experiments representing completely novel genetic backgrounds and environments—suggesting a remarkable degree of network completeness. Using this model we have constructed and tested hypotheses critical to this organism's interaction with its changing hypersaline environment. This study supports the claim that the high degree of connectivity within biological and EF networks will enable the construction of similar models for any organism from relatively modest numbers of experiments.

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