title: Molecular mechanisms of system responses to novel stimuli are predictable from public data.
abstract: 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.
authors.affiliation: Seattle Biomedical Research Institute, Seattle, WA 98109-5219 USA, Institute for Systems Biology, Seattle, WA 98109-5240 USA, The Key Laboratory of Developmental Genes and Human Disease, Ministry of Education, Institute of Life Science, Southeast University, Nanjing 210096, China and Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 704, Taiwan.
authors.forename: Samuel A
authors.initials: SA
authors.lastname: Danziger
authors.forename: Alexander V
authors.initials: AV
authors.lastname: Ratushny
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Smith
authors.forename: Ramsey A
authors.initials: RA
authors.lastname: Saleem
authors.forename: Yakun
authors.initials: Y
authors.lastname: Wan
authors.forename: Christina E
authors.initials: CE
authors.lastname: Arens
authors.forename: Abraham M
authors.initials: AM
authors.lastname: Armstrong
authors.forename: Katherine
authors.initials: K
authors.lastname: Sitko
authors.forename: Wei-Ming
authors.initials: WM
authors.lastname: Chen
authors.forename: Jung-Hsien
authors.initials: JH
authors.lastname: Chiang
authors.forename: David J
authors.initials: DJ
authors.lastname: Reiss
authors.forename: Nitin S
authors.initials: NS
authors.lastname: Baliga
authors.forename: John D
authors.initials: JD
authors.lastname: Aitchison
mesh_terms: D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D012441:Saccharomyces cerevisiae / Q000235:genetics; D049490:Systems Biology / Q000379:methods*