Heterogenous gene expression changes in bacterial communities are thought to produce subpopulations with particular survival benefits such as antibiotic tolerance. Currently, scientists have few tools to mimic and study these gene expression events in a controlled manner. The overall goal of this project is to develop light-based tools for precisely driving gene expression in targeted bacterial cells. The tools have the potential to reveal how heterogenous changes in gene expression impact the survival of subpopulations of bacterial cells. In addition to these scientific advances, the research program is complemented by educational and outreach activities. These include partnering with the Tumble Science Podcast for Kids to create episodes targeted for young audiences (ages 6-12). The project also provides training opportunities for undergraduate students and includes additional outreach efforts related to K-12 science education.

The technical goal of this project is to develop methods to precisely drive gene expression in single cells, and to test these techniques in the context of bacterial stress response. To achieve this, the researchers will use a high-throughput approach that uses automated microscopy, optogenetics, and microfluidics to implement real-time feedback control. Cellular processes are noisy and nonlinear, and can involve delays and differential responses between genetically identical cells. Thus, traditional control algorithms are either not accurate enough or too computationally expensive to be appropriate for large-scale analysis of gene regulatory network dynamics. This proposal takes a new approach, taking advantage of emerging control algorithms based on deep learning models. The researchers will conduct high-throughput experiments where optogenetic light signals are applied to many single cells to generate datasets for training a deep learning model. The deep learning model will then be used to implement feedback control in single cells using Deep Model Predictive Control to rapidly predict the optimal optogenetic stimulus to apply. The ultimate utility of these tools is in their ability to precisely control genes within biological networks. To test this potential, the researchers will focus on the acid stress response network. By using feedback control to impose dynamic profiles on network regulators, the researchers will study how signals propagate within the network and how cells use gene expression dynamics to hedge against the sudden appearance of stress.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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Boston University
United States
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