top of page

We are digitizing biological sensing and actuation by interfacing synthetic biology with microfluidics and advanced microcircuitry.

Asset 3new_SSB.png

Programming Biology

This project aims to leverage the memory of complex genetic systems in order to create smart biosensors. Bacterial or mammalian cells, living systems, will be able to “remember” things they have seen in their environment. Using these engineered cellular memory elements, Densmore and his team will create smart biosensors. By creating artificial environments using microfluidic devices, the team will then generate custom biological memory elements for those devices. The devices will be equipped with electronic technology that can detect and respond to biological changes. When these devices are in “swarms,” they can collectively act as smart biosensors in the environment.

These systems will be more vigorous, longer-lasting, and higher capacity than previously-published elements in the field. The devices will have custom-designed semiconductor-based electronics that are manufactured to measure biological outputs and use wireless communication to report results and control the microfluidic operations. 

Scientific Work

We aim to build out these custom bioelectronic microfluidic hybrid systems in three main phases:


TEST: By using high-throughput droplet microfluidics, we will screen biological designs and operational conditions to identify promising candidates


REFINE: Using medium-throughput continuous flow microfluidics, we will directly interface candidate designs with low-power electronics to further select optimal memory circuits


DEPLOY: Final memory circuits will be tested inside an aquatic environment to evaluate its efficacy as a deployable biosensor, without the need for human operation


  • McIntyre, D., Lashkaripour, A., Fordyce, P. and Densmore, D., 2022. Machine learning for microfluidic design and control. Lab on a Chip, 22(16), pp.2925-2937.

  • Arguijo, D. and Densmore, D., 2022. Efficient Droplet Microfluidic Characterization for Design Automation. International Workshop on Bio-Design Automation (IWBDA).

  • Bragdon, M., Patel, N., Chuang, J., Levien, E., Bashor, C. and Khalil, A. S., 2022. Cooperative assembly confers regulatory specificity and long-term genetic circuit stability. bioRxiv. doi: 10.1101/2022.05.22.492993. 

  • Huang, T.P., Heins, Z.J., Miller, S. M., Wong, B. G., Balivada, P. A., Wang, T., Khalil, A. S., and Liu, D. R., 2022. High-throughput continuous evolution of compact Cas9 variants targeting single-nucleotide-pyrimidine PAMs. Nature Biotechnology, 41, 96- 107 (2023). doi: 10.1038/s41587-022-01410-2.

  • Sanford, A., Kiriakov, S., and Khalil, A. S., 2022. A Toolkit for Precise, Multigene Control in Saccharomyces cerevisiae, 11: 3912-3920. doi: 10.1021/acssynbio.2c00423.

  • Shaw, W.M., Zhang, Y., Lu, X., Khalil, A. S., Ladds, G., Luo, X., and Ellis, T., 2022. Screening microbially produced D9- tetrahydrocannabinol using a yeast biosensor workflow. Nature Communications, 13: 5509. doi: 10.1038/s41467-022-33207-x.

  • Bragdon, M., Patel, N., Chuang, J., Levien, E., Bashor, C. and Khalil, A. S., 2023. Cooperative assembly confers regulatory specificity and long-term genetic circuit stability. Cell, 186: 3810-3825. doi: 10.1016/j.cell.2023.07.012. 

  • McIntyre, D., Lashkaripour, A., Arguijo, D., Fordyce, P., and Densmore, D. Versatility and stability optimization of flow-focusing droplet generators via quality metric-driven design automation. Accepted in Lab on a Chip.

  • Liu, Q., Arguijo, D., Yasar, A., Caygara, D., Kassem, A., Densmore, D., and Yazicigil, R. T. Droplet microfluidics co-designed with real-time CMOS luminescence sensing and impedance spectroscopy of 4 nL droplets at a 67 mm/s velocity. Accepted with a live demonstration at 2024 IEEE International Solid-State Circuits Conference (ISSCC).

  • McIntyre, D., Arguijo, D., Kawata, K., and Densmore, D. Component library creation and pixel array generation with micromilled droplet microfluidics. In Submission.

bottom of page