Chan Zuckerberg Biohub, San Francisco, United States; Department of Physics, University of California, Berkeley, Berkeley, United States; Biophysics Graduate Group, University of California, Berkeley, Berkeley, United States; Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States; Institute for Quantitative Biosciences–QB3, University of California at Berkeley, Berkeley, United States
A challenge in quantitative biology is to predict output patterns of gene expression from knowledge of input transcription factor patterns and from the arrangement of binding sites for these transcription factors on regulatory DNA. We tested whether widespread thermodynamic models could be used to infer parameters describing simple regulatory architectures that inform parameter-free predictions of more complex enhancers in the context of transcriptional repression by Runt in the early fruit fly embryo. By modulating the number and placement of Runt binding sites within an enhancer, and quantifying the resulting transcriptional activity using live imaging, we discovered that thermodynamic models call for higher-order cooperativity between multiple molecular players. This higher-order cooperativity captures the combinatorial complexity underlying eukaryotic transcriptional regulation and cannot be determined from simpler regulatory architectures, highlighting the challenges in reaching a predictive understanding of transcriptional regulation in eukaryotes and calling for approaches that quantitatively dissect their molecular nature.
