(568k) Quantitative Determination of Transcription Factor Profiles From Reporter Data
The regulation of gene expression by transcription factors through different expression and activation dynamics is an important aspect of genomics and systems biology. Reporter systems using green fluorescent protein (GFP) or luciferase are often used to infer transcription factor dynamics. We recently used an inverse problem solution of GFP reporter profiles to demonstrate that the activation dynamics of a model transcription actor (NF-κB) can be reconstructed from GFP data. This approach assumes that the general nature of the transcription factor dynamics is accurately known; however, it is non-trivial to determine the exact nature of the transcription factor dynamics as it often depends upon the cell type and the stimulus used to activate the transcription factor. This, in turn, limits the determination of accurate transcription factor dynamics from reporter data. To address this point, we developed a reporter cell line for expressing GFP using an inducible, artificial transcription factor (tTA) and minimal promoter system. The artificial transcription factor can be activated independent of the cellular regulatory machinery through addition of doxycycline. This allows us to directly control the dynamics of the artificial transcription factor, and thereby, develop a model describing its activation dynamics from reporter data. In our previous work, we used an inverse problem solution of GFP reporter profiles to demonstrate proof-of-concept that the activation dynamics of NF-κB, a transcription factor whose dynamics are well-described in the literature (1, 3), can be reconstructed from fluorescence data (2, 4). We expanded upon this approach and developed a method for predicting the activation dynamics of transcription factors whose activation dynamics can have a variety of different forms that can be experimentally controlled. We used a commercially available artificial transcription factor (tTA) reporter system to generate different activation profiles (step function response and pulse response) of tTA through addition of a chemical inducer in a manner that is independent of the cellular regulatory machinery. Using the resultant GFP reporter data, we demonstrated that tTA activation profiles can be accurately determined from GFP reporter profiles. This integrated experimental-computational approach can be useful for quantitatively determining transcription factor dynamics and update models of signal transduction pathways.
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