The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors
WashU Affiliated Authors: Christopher J. Hartline (Dept. of Energy, Environmental and Chemical Engineering) , Fuzhong Zhang (Dept. of Energy, Environmental and Chemical Engineering, Division of Biology & Biomedical Sciences, Institute of Materials Science & Engineering)
Abstract: Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.
Citation or DOI: The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors, Christopher J. Hartline and Fuzhong Zhang, ACS Synthetic Biology, 2022, 11 (7), 2247-2258, DOI: 10.1021/acssynbio.2c00143