The experimental platforms are the study subjects in the lab’s research. Not only do these platforms produce experimental measurements that propel the advancement of the foundry, they also support the development of one another.
The Experimental Platforms
Silicon-Cell Communications
We combine the power of biochemical signal sensing from engineered bacterial cells and the remote reporting capability from the CMOS chips. This technology opens the possibility to assay system dynamics in uncharted locations like the human gut, underground soil, and even outer space.
*Pursued in collaboration with the MICS Lab at Caltech.
Microbial Populational Control
We engineer smart and self-contained synthetic microbial cross-feeders to influence natural immune cells and microbial communities. The systems design and feedback control is a highly complex problem due to the interwoven population and biomolecular dynamics across scales. We utilize multi-scale dynamical models to guide the experimental designs.
Current Funding: ARPA-H
Cell-free Systems
Cell-free systems harvest the essential components for gene expression from cells and execute the reaction in vitro. This technology offers the possibility to decipher complex gene expression dynamics in a simplified environment. It is a vital tool in model development and system identification. We also utilize it to guide the design and engineering of dynamical systems in living cells.
Publications
Hu, C. Y. et al. A Field-Deployable Arsenic Sensor Integrating Bacillus Megaterium with CMOS Technology. bioRxiv2024.07.18.604150 (2024) doi:10.1101/2024.07.18.604150.
Aghlmand, F., Hu, C.Y, Sharma, S., Pochana, K., Murray, R.M, and Emami, A., “A 65-nm CMOS Fluorescence Sensor for Dynamic Monitoring of Living Cells”. IEEE J. Solid-State Circuits 1–17 (2023) doi:10.1109/jssc.2023.3308853.3.
Aghlmand, F., Hu, C.Y, Sharma, S., Pochana, K., Murray, R.M, and Emami, A., “A 65nm CMOS Living-Cell Dynamic Fluorescence Sensor with 1.05fA Sensitivity at 600/700nm Wavelengths,” in IEEE Int. Solid-State Circuits Conf., Feb. 2023
Hu, C. Y., & Murray, R. M. Layered Feedback Control Overcomes Performance Trade-off in Synthetic Biomolecular Networks. Nat Commun 13, 5393 (2022). https://doi.org/10.1038/s41467-022-33058-6 (Corresponding Author)
Green, L. N., Hu, C. Y., Ren, X. Y., & Murray, R. M. (2019). Bacterial Controller Aided Wound Healing: A Case Study in Dynamical Population Controller Design. bioRxiv,19(7), 3217–15. http://doi.org/10.1101/659714
Hu, C. Y., & Murray, R. M.. Design of a genetic layered feedback controller in synthetic biological circuitry. bioRxiv, 2019. 2(3) 905–911. https://doi.org/10.1101/647057. (Corresponding author)
Hu, C. Y., Takahashi, M. K., Zhang, Y., & Lucks, J. B. (2018). Engineering a Functional Small RNA Negative Autoregulation Network with Model-Guided Design. ACS Synthetic Biology, 7(6), 1507–1518. http://doi.org/10.1021/acssynbio.7b00440
Hu, C. Y., Varner, J. D., & Lucks, J. B. (2015). Generating Effective Models and Parameters for RNA Genetic Circuits. ACS Synthetic Biology, 4(8), 914–926. http://doi.org/10.1021/acssynbio.5b00077