(665e) Decoding the Complexity of Metabolite-Responsive Transcriptional Factors: Cross-Talk, Auto-Regulation and Feedback Control

A grand challenge in synthetic biology is to design and engineer predictable transcriptional output driving different level of cellular/metabolic activity. Metabolite-responsive transcriptional factors (MRTFs) have emerged to address this challenge. These biosensors translate an internal cellular signal to a transcriptional reporter output. Reductionist tends to simplify protein-DNA-RNA interactions and overlook cellular-context effects, leading to undesirable gene expression dynamics with perplexing signal outputs. In this talk, I will present a few examples how mathematical models could help understand the design constrains of MRTFs, with the aim to improve the robustness and predictability of the engineered system. Specifically, we developed a mechanism-based kinetic model to simulate and predict how transcriptional factor (TF) cross-talk, autoregulation and feedback reshape the transcriptional dynamics of an engineered sensor-reporter system. Our computational framework allows us to investigate the effects of protein cooperativity, DNA binding affinity, non-cognate DNA cross-talk, cell growth dilution and autoregulation et al. The computational insights obtained in this study will guide us to design more accurate, sensitive TF-based biosensors and may serve as a diagnostic platform to troubleshoot the complex transcriptional dynamics in biosensor design.