Models of mammalian regulatory networks controlling gene expression have been inferred from genomic data, yet have largely not been validated. We present an unbiased strategy to systematically perturb candidate regulators and monitor cellular transcriptional responses. We apply this approach to derive regulatory networks that control the transcriptional response of mouse primary dendritic cells (DCs) to pathogens. Our approach revealed the regulatory functions of 125 transcription factors, chromatin modifiers, and RNA binding proteins and constructed a network model consisting of two dozen core regulators and 76 fine-tuners that help explain how pathogen-sensing pathways achieve specificity. This study establishes a broadly-applicable, comprehensive and unbiased approach to reveal the wiring and functions of a regulatory network controlling a major transcriptional response in primary mammalian cells.

Protein expression is regulated by production and degradation of mRNAs and proteins, but their specific relationships remain unknown. We combine measurements of protein production and degradation and mRNA dynamics to build a quantitative genomic model of the differential regulation of gene expression in LPS-stimulated mouse dendritic cells. Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels. Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for over half of the absolute change in protein molecules in the cell. Thus, the proteome is regulated by transcriptional induction of novel cellular functions and remodeling of preexisting functions through the protein life cycle.